CN109413068A - A kind of wireless signal encryption method based on antithesis GAN - Google Patents
A kind of wireless signal encryption method based on antithesis GAN Download PDFInfo
- Publication number
- CN109413068A CN109413068A CN201811268894.7A CN201811268894A CN109413068A CN 109413068 A CN109413068 A CN 109413068A CN 201811268894 A CN201811268894 A CN 201811268894A CN 109413068 A CN109413068 A CN 109413068A
- Authority
- CN
- China
- Prior art keywords
- wireless signal
- network
- size
- decryption
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Facsimile Transmission Control (AREA)
Abstract
The invention discloses a kind of wireless signal encryption methods based on antithesis GAN, comprising: using wireless signal Encryption Model by original wireless signal encryption at coded signal image；Using wireless signal decrypted model by coded signal image decryption at decryption wireless signal；The wireless signal Encryption Model and wireless signal decrypted model are obtained by the training of following model training system, the model training system includes: wireless signal refined net Ag, wireless signal encryption differentiation network A d, wireless signal decryption network Bg, wireless signal decryption differentiation network B d, wireless signal sorter network C, pre-training to Ag and C and the joint training to Ag, Ad, Bg, Bd and C are used to above-mentioned model training system, to determine wireless signal Encryption Model and wireless signal decrypted model.Wireless signal effectively can be encrypted as picture by this method, improve the concealment of information transmission.
Description
Technical field
The invention belongs to the security fields research fields of the deep learning algorithm of artificial intelligence field and data, and in particular to
A kind of wireless signal encryption method based on antithesis GAN.
Background technique
Currently, deep learning has been widely used in the fields such as target detection, image detection, data generation.It generates
Fighting network (GAN) is one of technology relatively advanced in current deep learning, generates confrontation network and passes through in frame (at least)
Two modules: the mutual game of model (Generative Model) and discrimination model (Discriminative Model) are generated
Study generates preferable output.According to different network structures, a series of GAN network has been derived: DCGAN, WGAN,
WGAN-GP、LSGAN-BEGAN、DUAL-GAN。
Wherein, generation confrontation network (DUAL-GAN) of antithesis GAN be on the basis of original generation fights network into
One step is extended to two generation to intercouple confrontation networks, wherein there are two generator and two arbiters, with text and figure
For the mutual translation of piece, wherein character translation can be picture by first generator Ag, corresponding also one
A arbiter Ad can identify the true and false of picture.Meanwhile a corresponding generator Bg is constructed, photo can be translated as
Whether text, same generator Bg also have a corresponding arbiter Bd accurate to identify text.
Current existing wireless signal encryption method is broadly divided into symmetric cryptography and divides symmetric cryptosystem.Symmetric cryptography is adopted
It is that the encryption of asymmetric encoding technological document and decryption use identical key.Asymmetric encryption needs two keys: open
Key and private cipher key carry out encrypting another key being decrypted using one of key.Existing encryption method exists
It is easily attacked, the problems such as encryption mode is single.Under normal circumstances, wireless signal encryption technology is by the way of digital encryption, this
It is bigger that kind mode calculates cost, and is easy to be trapped utilization.
Summary of the invention
The object of the present invention is to provide a kind of wireless signal encryption methods based on antithesis GAN, pass through the generation of antithesis GAN
It fights network and generates picture corresponding to wireless signal, and classified using wireless signal disaggregated model C, thus to wireless
Signal data realizes the process of encryption.The wireless signal encryption method improves the confidentiality and peace of wireless signal transmission process
Quan Xing.
For achieving the above object, the present invention the following technical schemes are provided:
A kind of wireless signal encryption method based on antithesis GAN, comprising:
Using wireless signal Encryption Model by original wireless signal encryption at coded signal image；
Using wireless signal decrypted model by coded signal image decryption at decryption wireless signal；
The wireless signal Encryption Model and wireless signal decrypted model are obtained by the training of following model training system, institute
Stating model training system includes:
Wireless signal refined net Ag, for encrypting to original wireless signal, input is original wireless signal, nothing
The decryption wireless signal of line decrypted signal network B g output, exports as coded signal image；
Wireless signal encryption differentiates network A d, and input is normal color image, wireless signal refined net Ag output
Coded signal image exports as the judgement to normal color image and coded signal image as a result, wherein utilizing wireless signal
When encryption differentiates that the output feedback training wireless signal encryption of network A d differentiates network A d parameter, the class calibration of normal color image
Justice be 1, the category of coded signal image is defined as 0, using wireless signal encryption differentiate network A d output feedback training without
When line signal encryption differentiates network A g parameter, coded signal image is used only, and the category of coded signal image is defined as at this time
1；
Wireless signal decryption network Bg, for coded signal image to be decrypted, input is normal color image, nothing
The coded signal image of line signal encryption network A g output exports to decrypt wireless signal；
Wireless signal decryption differentiates network B d, and input is original wireless signal, wireless signal decryption network Bg output
Wireless signal is decrypted, is exported as the judgement to original wireless signal and decryption wireless signal as a result, wherein utilizing wireless signal
When decryption differentiates that the output feedback training wireless signal decryption of network B d differentiates network B d parameter, the class calibration of original wireless signal
Justice be 1, the category for decrypting wireless signal is defined as 0, using wireless signal decryption differentiate network B d output feedback training without
When line decrypted signal network B g parameter, decryption wireless signal is used only, and the category for decrypting wireless signal at this time is defined as 1；
Wireless signal sorter network C, input are coded signal image and the life of wireless signal refined net Ag output
At the category of the wireless signal of the coded signal image, the i.e. modulation type of wireless signal, the classification knot for coded signal is exported
Fruit, each classification results correspond to the modulation type of original wireless signal；
Training process are as follows:
Stage one: pre-training, using original wireless signal and corresponding wireless signal modulation type category to wireless signal
Refined net Ag and wireless signal sorter network C carries out pre-training；
Stage two: retraining, firstly, fixed wireless signal encryption differentiate network A d, wireless signal decryption differentiate network B d,
The parameter of wireless signal sorter network C, the parameter of joint training wireless signal refined net Ag and wireless signal decryption network Bg；
Then, the parameter of fixed wireless signal encryption network A g, wireless signal decryption network Bg, wireless signal sorter network C, joint instruction
Practice the parameter that wireless signal encryption differentiates network A d and wireless signal decryption differentiates network B d；Finally, fixed wireless signal encryption
Differentiate the parameter of network A d, wireless signal decryption network Bg, wireless signal decryption differentiation network B d, joint training wireless signal adds
The parameter of close network A g and wireless signal sorter network C；By above three joint training, until wireless signal refined net Ag
Network A d is differentiated with wireless signal encryption, and it is assorted equal that wireless signal decryption network Bg and wireless signal decryption differentiate that network B d realization is received
Weighing apparatus, training are completed, and trained wireless signal refined net Ag is wireless signal Encryption Model, trained wireless signal decryption
Network B g is wireless signal decrypted model.
The graded combination Training strategy provided through the invention, the wireless signal refined net Ag after completing training can make
It is signal pattern by wireless signal data encryption for a private cipher key of wireless signal；Wireless signal decryption network Bg can be with
Coded signal image decryption is wireless signal data by another key as encrypted signal pattern, and by introducing
Wireless signal sorter network C, classifies to the wireless signal for using different modulating type to obtain, and wireless signal can be made to encrypt
Network A g learns the characteristic of division attribute of wireless signal modulation type simultaneously in the training process, can be to different modulating type
Wireless signal is preferably encrypted.
The effect that the wireless signal encryption method based on antithesis GAN has are as follows: wireless signal can be passed through into wireless communication
Number Encryption Model projects image feature space from signal characteristic space, picture signal is encrypted as automatically, to escape wireless communication
The identification of number identifying system.The concealment of radio signal propagation, the safety of information transmission can be improved by this encryption method
Property.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art, can be with root under the premise of not making the creative labor
Other accompanying drawings are obtained according to these attached drawings.
Fig. 1 is the structural schematic diagram for the model training system that embodiment provides；
Fig. 2 is the structural schematic diagram for the wireless signal refined net Ag that embodiment provides；
Fig. 3 is the structural schematic diagram for the wireless signal decryption network Bg that embodiment provides；
Fig. 4 is the structural schematic diagram that the wireless signal encryption that embodiment provides differentiates network A d；
Fig. 5 is the structural schematic diagram that the wireless signal decryption that embodiment provides differentiates network B d；
Fig. 6 is the structural schematic diagram for the wireless signal sorter network C that embodiment provides；
12 class wireless signals are encrypted to picture in the classification method training process that Fig. 7 (a)~7 (l) provides for embodiment
Visualization result schematic diagram.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, with reference to the accompanying drawings and embodiments to this
Invention is described in further detail.It should be appreciated that the specific embodiments described herein are only used to explain the present invention,
And the scope of protection of the present invention is not limited.
For the confidentiality for improving wireless signal transmission, present embodiments provides a kind of wireless signal based on antithesis GAN and add
Decryption method, specific steps include: by original wireless signal encryption using wireless signal Encryption Model into coded signal image；It utilizes
Wireless signal decrypted model is by coded signal image decryption at decryption wireless signal.
The wireless signal Encryption Model and wireless signal decrypted model are obtained using model training system as shown in Figure 1
, specific model training system includes five modules: being respectively wireless signal refined net Ag, wireless signal encryption differentiation net
Network Ad, wireless signal decryption network Bg, wireless signal decryption differentiate network B d and wireless signal sorter network C.
Wireless signal refined net Ag is mainly used for encrypting wireless signal, i.e., is to add by original wireless signal encryption
Close signal pattern.Wireless signal refined net Ag is the neural network being made of LSTM unit, convolutional layer and full articulamentum, knot
Structure is as shown in Figure 2, comprising: the wireless signal being originally inputted is having a size of [512,2], wherein when the sampling of 512 expression wireless signals
Between point, 2 indicate that the characteristic value of wireless signal each time points, training process are minimum using the training of most small quantities of gradient descent method
Generally be taken as 64 per a batch of wireless signal data sample number in batch, obtained after LSTM unit having a size of [512,
128] characteristic layer, wherein 512 correspond to original time point, the feature vector that 128 corresponding each time points are calculated makes
The characteristic layer having a size of 128 is obtained with full connection, the characteristic layer having a size of 128 is obtained using full connection, is obtained using full articulamentum
To the characteristic layer having a size of 64*64*3, this feature layer is deformed to obtain having a size of [64,64,3] using reshape function
Characteristic layer, wherein 64 respectively correspond the length and width of characteristic layer, the depth of 3 character pair layers, using having a size of [5,5,64]
Convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the characteristic layer having a size of [32,32,64], use
[5,5,128] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length are obtained having a size of [16,16,128]
Characteristic layer, the maximum pond module for being 2 using the convolution module having a size of [5,5,256] and having a size of [2,2] step-length obtain ruler
The very little characteristic layer for [8,8,256], the maximum for being 2 using the convolution module having a size of [5,5,512] and having a size of [2,2] step-length
Pond module obtains the characteristic layer having a size of [4,4,512], using having a size of [5,5,512] convolution module and having a size of [2,
2] the maximum pond module that step-length is 2 obtains the characteristic layer having a size of [2,2,512], uses the convolution having a size of [5,5,512]
Module and having a size of [2,2] step-length be 2 maximum pond module obtain the characteristic layer having a size of [1,1,512], using having a size of
The warp volume module of [5,5,512] obtains the characteristic layer having a size of [2,2,512], the characteristic layer of [2,2,512] before combining,
The characteristic layer having a size of [2,2,1024] is obtained, the warp volume module of [5,5,512] is used to obtain the characteristic layer of [4,4,512],
The characteristic layer of [4,4,512] before joint obtains the characteristic layer of [4,4,1024], uses the warp volume module of [5,5,256]
The characteristic layer of [8,8,256] is obtained, the characteristic layer of [8,8,256] before combining obtains the characteristic layer of [8,8,512], uses
The warp volume module of [5,5,128] obtains the characteristic layer of [16,16,128], and the characteristic layer of [16,16,128] before combining obtains
To the characteristic layer of [16,16,256], the warp volume module of [5,5,64] is used to obtain the characteristic layer of [32,32,64], before joint
[32,32,64] characteristic layer, obtain the characteristic layer of [32,32,128], use the warp volume module of [5,5,3] obtain [64,
64,3] characteristic layer, what is obtained is the picture of corresponding ImageNet64, and picture a length of 64, width 64 includes RGB tri-
Channel.
Wireless signal decryption network Bg is mainly used for that coded signal image is decrypted, i.e., by coded signal image decryption
To decrypt wireless signal.Wireless signal decryption network Bg is the neural network being made of convolutional layer and full articulamentum, and structure is such as
Shown in Fig. 3, comprising: the picture size being originally inputted is [64,64,3], uses convolution module and size having a size of [5,5,64]
The maximum pond module for being 2 for [2,2] step-length obtains the characteristic layer having a size of [32,32,64], using having a size of [5,5,128]
Convolution module and having a size of [2,2] step-length be 2 maximum pond module obtain making having a size of the characteristic layer of [16,16,128]
With having a size of [5,5,256] convolution module and having a size of [2,2] step-length be 2 maximum pond module obtain having a size of [8,8,
256] characteristic layer, the maximum pond module for being 2 using the convolution module having a size of [5,5,1024] and having a size of [2,2] step-length
The characteristic layer having a size of [4,4,1024] is obtained, using the convolution module having a size of [5,5,2048] and having a size of [2,2] step-length
The characteristic layer having a size of [2,2,2048] is obtained for 2 maximum pond module, uses the convolution module having a size of [5,5,4096]
It obtains using full link block having a size of the characteristic layer of [1, Isosorbide-5-Nitrae 096] for 2 maximum pond module with having a size of [2,2] step-length
The characteristic layer having a size of 16384 (512*32) is obtained, obtains the characteristic layer having a size of 4096 (512*8) using full link block,
The characteristic layer having a size of 1024 (512*2) is obtained using full link block, is become its shape using reshape function
[512,2] obtain wireless signal corresponding to picture, wherein 512 corresponding sampling time points, the feature of 2 corresponding each time points
Value.
Wireless signal encryption differentiates that network A d is mainly used for carrying out two classification to normal color image and coded signal image,
Judge the category of normal color image and coded signal image, the class for defining normal color image is designated as true (real), encryption
The class of signal pattern is designated as false (fake), is encrypted by classification results feedback training (feedback training) wireless signal
Network A g, the image for generating encryption are more nearly true value.Wireless signal encryption differentiates that network A d is by convolutional layer and Quan Lian
The neural network of layer composition being connect, structure is as shown in Figure 4, comprising: the picture size of input is [64,64,3], use [5,5,
64] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the characteristic layer of [32,32,64], use [5,
5,128] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the characteristic layer of [16,16,128], make
The maximum pond module for being 2 with the convolution module of [5,5,256] and having a size of [2,2] step-length obtains the characteristic layer of [8,8,256],
It uses the convolution module of [5,5,512] and obtains the feature of [4,4,512] having a size of [2,2] step-length for 2 maximum pond module
Layer uses the convolution module of [5,5,1] and obtains the feature of [1,1,1] having a size of [4,4] step-length for 4 maximum pond module
Layer, the matrix of obtained not normalized confidence value identical with shape 0 or 1 do the coded signal that cross entropy calculates Ag output
The gap of image and normal color image.Wireless signal encryption differentiates that the input of network A d is the normal color of ImageNet64
The mixing for the coded signal image that image and Ag encryption generate, exports as the judgement to normal color image and coded signal image
As a result.
Wireless signal decryption differentiates that network B d is mainly used for carrying out two classification to original wireless signal and decryption wireless signal,
Judge original wireless signal and decrypt the category of wireless signal, the class for defining original wireless signal is designated as true (real) and decryption
The class of wireless signal is designated as false (fake), and passes through classification results feedback training (feedback training) wireless signal solution
Close network B g, the wireless signal for generating decryption are more nearly true value.Wireless signal decryption differentiates that network B d is mono- by LSTM
The neural network that member, convolutional layer and full articulamentum are constituted, structure are as shown in Figure 5, comprising: the wireless signal sample being originally inputted
Having a size of [512,2], the characteristic layer having a size of [512,64] is obtained after LSTM unit, wherein 512 when corresponding to original
Between point, the feature vector that 64 corresponding each time points are calculated obtains the characteristic layer having a size of 64 using full connection, using complete
Connection obtains the characteristic layer having a size of 1, and obtained not normalized confidence value and 0 or 1 is done cross entropy calculating wireless signal and added
The gap of original wireless signal and decryption wireless signal that close network A g is generated.Wireless signal decryption differentiates the input of network B d
It is decrypted into the mixing of decryption wireless signal for normal wireless signal and Ag, exports as to normal wireless signal and decryption wireless signal
Judging result.
Wireless signal sorter network C is mainly used for classifying to wireless signal, inputs as wireless signal refined net Ag
The coded signal image of output, exports the classification results for coded signal, finely tunes training (fine tune by feedback
Training) Ag, it is more accurate to wireless signal refined net Ag to make.Wireless signal sorter network C is by convolutional layer and Quan Lian
The neural network of layer composition is connect, specific structure is as shown in Figure 6, comprising: the dimension of picture of input is [64,64,3], is used
Convolution kernel having a size of [5,5,64] and the maximum pond module for being 2 having a size of [2,2] step-length are obtained having a size of [32,32,64]
Characteristic layer, using having a size of [5,5,128] convolution kernel and having a size of [2,2] step-length be 2 maximum pond module obtain ruler
The very little characteristic layer for [16,16,128], the maximum for being 2 using the convolution kernel having a size of [5,5,256] and having a size of [2,2] step-length
Pond module obtains the characteristic layer having a size of [8,8,256], using the convolution kernel having a size of [5,5,512] and having a size of [2,2]
The maximum pond module that step-length is 2 obtains the characteristic layer having a size of [4,4,512], obtains the spy having a size of 1024 using full connection
Layer is levied, full articulamentum is used to obtain (being directed to comprising 12 kinds of wireless signal modulation types having a size of 12 characteristic layer as output
12 classification tasks), the class of obtained not normalized confidence value original wireless signal corresponding with coded signal image
Mark does cross entropy and calculates distance.
The design object of above-mentioned model training system are as follows:
(a) original wireless signal is encrypted automatically by network A g, which adds respectively as wireless signal
The close input sample for differentiating network A d and wireless signal sorter network C, and Ag is fed back to by the output of Ad and C, adjust Ag's
Inner parameter.Wherein Ad is by determining the picture sample of Ag encryption generation and the distribution distance of true normal sample, to adjust Ag
Parameter；C is by predicting category at a distance from true category, to adjust Ag parameter.Eventually by the multilateral Game mistake of Ag and Ad and C
Journey guarantees the stability for the picture sample that Ag encryption generates, and is trained with the encryption picture generated to C that obtaining can be right
The wireless signal Encryption Model that the wireless signal data of different modulating type are more preferably encrypted；
(b) it is automatically signal data to image data decryption by network B g, is decrypted as wireless signal and differentiate network B d
Input sample adjust Bg inner parameter by the input feedback of Bd to Bg.The wherein signal that Bd is generated by determining Bg decryption
The distribution distance of sample and authentic specimen, to adjust Bg parameter.
In model training system, the network structure of wireless signal sorter network C is related with the complexity of data set；And
The network structure of Ag and Ad designs, related with the complexity of C network and data set；Bg and Bd network structure design, with number
It is related according to the complexity of collection.In order to realize better cipher round results, and prevent in the training process generation model collapse
It bursts, be better achieved the encryption and decryption of wireless model, present invention uses the methods of joint training to have carried out accordingly model
Training.
Specifically, the training of above-mentioned model training system is broadly divided into two stages of pre-training and retraining, detailed process
Are as follows:
The pre-training stage: wireless signal is encrypted using original wireless signal and corresponding wireless signal modulation type category
Network A g and wireless signal sorter network C carries out pre-training.When training, fixed wireless signal encryption differentiates network A d, wireless communication
Number decryption differentiate network B d and wireless signal decryption network Bg parameter, trained epochs=N1, i.e. training data are set
Collection is used N1 times；The input of wireless signal refined net Ag is original wireless signal, is exported as encrypted coded signal figure
Picture, the input of wireless signal sorter network C are the coded signal image of wireless signal refined net Ag output, are exported as to encryption
The category of the debug-type of original wireless signal corresponding to signal pattern is predicted；Wireless signal refined net Ag and nothing at this time
Disaggregated model Ag-C classifies to the modulation type of wireless signal to line Modulation recognition network C as a whole, wireless signal
Refined net Ag is equivalent to the characteristic extracting module of disaggregated model Ag-C, and wireless signal sorter network C is equivalent to disaggregated model Ag-
The categorization module of C.
Specifically, original signal data collection x is inputtedsignal, corresponding picture Ag (x is generated by Ag networksignal),
By Ag (xsignal) be input in disaggregated model C, 10 epochs of training.
The retraining stage, using joint training strategy, detailed process are as follows:
(1) fixed wireless signal encryption differentiates that network A d, wireless signal decryption differentiate network B d, wireless signal sorter network
The parameter of C, by original wireless signal xsignal, wireless signal decryption network Bg output decryption wireless signal Bg (ximage) conduct
The input of wireless signal refined net Ag, the parameter of training Ag, the coded signal image for exporting it are more nearly normal color
Image, so that the ciphering process of wireless signal is realized, so that the propagation that encrypted information is safer；And by normal color figure
As ximage, wireless signal refined net Ag output coded signal image Ag (xsignal) as wireless signal decryption network Bg's
Input, the parameter of training Bg, the decryption wireless signal for exporting it are more nearly original wireless signal, realize that encryption is wireless with this
The decrypting process of signal；
The optimization aim of the process indicates are as follows:
Wherein, xAgIndicate the coded signal image of wireless signal refined net Ag output, xBgIndicate that wireless signal decrypts net
The decryption wireless signal of network Bg output, xAg~pAg indicates xAgSample the output from wireless signal refined net Ag, xBg~pBg table
Show xBgThe output from wireless signal decryption network Bg is sampled, pAg and pBg respectively indicate the probability distribution of Ag and Bg output, Ad
(xAg) indicate that wireless signal encryption differentiates network A d to xAgDifferentiation probability, Bd (xBg) indicate that wireless signal decryption differentiates network
Bd is to xBgDifferentiation probability, E () indicate cross entropy expectation；
(2) parameter of fixed wireless signal encryption network A g, wireless signal decryption network Bg, wireless signal sorter network C,
The coded signal image Ag (x that wireless signal refined net Ag is exportedsignal) and normal color imageBlended data,
Encrypt the input for differentiating network A d as wireless signal, the parameter of training Ad can distinguish coded signal image and normal
Color image；And the decryption wireless signal Bg (x for exporting wireless signal decryption network Bgimage) and original wireless signal
Blended data, the input for differentiating network B d is decrypted as wireless signal, it is wireless can to distinguish decryption for the parameter of training Bd
Signal and original wireless signal；
The optimization aim of the process indicates are as follows:
Wherein, Pdata indicates raw data set,Indicate normal signal sampleFrom original letter
Number Pdata；Indicate normal image dataFrom raw image data Pdata；
(3) fixed wireless signal encryption differentiates that network A d, wireless signal decryption network Bg, wireless signal decryption differentiate network
The parameter of Bd, by original wireless signal xsignal, wireless signal decryption network Bg output decryption wireless signal Bg (ximage) conduct
The input of wireless signal refined net Ag, and the coded signal image Ag (x that wireless signal refined net Ag is exportedsignal) and
Corresponding true category y (namely original wireless signal xsignalCorresponding modulation type category) as wireless signal classification net
The input of network C, the parameter of coorinated training wireless signal refined net Ag and wireless signal sorter network C；
The optimization aim of the process are as follows:
Wherein, y indicates the modulation type category of original wireless signal,Indicate normal wireless sample of signalFrom wireless signal data set, C () indicates wireless signal sorter network C to the classification knot of wireless signal modulation type for sampling
Fruit；
Wireless signal classification mould network C is introduced, is classified to the wireless signal for using different modulating type to obtain, energy
Enough make wireless signal refined net Ag in the training process while learning the characteristic of division attribute of wireless signal modulation type, it can be with
It is preferably encrypted for the modulation type of wireless signal.
(4) step (1)~(3) are repeated, until wireless signal refined net Ag and wireless signal encryption differentiate network A d, nothing
Line decrypted signal network B g and wireless signal decryption differentiate that network B d realizes Nash Equilibrium, i.e. wireless signal refined net Ag and nothing
Line style number encryption differentiates that both network A d game tends to balance, and wireless signal decryption network Bg and wireless signal decryption differentiate network
The game of both Bd tends to balance, training cut-off, and trained wireless signal refined net Ag is wireless signal Encryption Model, instruction
The wireless signal decryption network Bg perfected is wireless signal decrypted model.
When training, the loss function being related to is respectively as follows:
The loss function Ag_loss of wireless signal refined net Ag are as follows:
Wherein, λBFor introduce parameter, for balance B_loss withCodomain range so that
B_loss withCodomain it is close, xAg~pAg indicates xAgIt samples from wireless signal refined net Ag
Output, B_loss indicate normal color image and coded signal image mean square error, form are as follows:
B_loss=MSE (image, Ag (xsignal))
Wherein, image indicates the normal color image on ImageNet64, the normal color image and image Ag
(xsignal) it is the consistent two images of pattern, MSE () indicates mean square error function；
The loss function Bg_loss of wireless signal decryption network Bg are as follows:
Wherein, λAFor introduce parameter, for balance A_loss withCodomain range so that
A_loss withCodomain it is close, A_loss indicates original wireless signal and decrypts wireless signal
Mean square error, form are as follows:
A_loss=MSE (signal, Bg (ximage))
Wherein, signal indicates original wireless signal；
Wireless signal encryption differentiates the loss function Ad_loss of network A d are as follows:
Wireless signal decryption differentiates the loss function Bd_loss of network B d are as follows:
The loss function C_loss of wireless signal sorter network C are as follows:
The calculating and optimization of the 5 above loss functions are converted the calculating of following 3 loss functions by the present embodiment:
G_loss=Ag_loss+Bg_loss
D_loss=Ad_loss+Bd_loss
C_loss
By the calculating to above 3 loss functions, each network is updated by backpropagation using Adam optimizer
Parameter.
Have when realizing Nash Equilibrium:
Through the above training method based on minimax theorem (Minimax theorem), Ag and Bg, Ad are realized
With the competition Game Relationship of Bd, C tripartite, the target of Ag is quickly to encrypt a large amount of wireless signal sample, can be connect as far as possible
Nearly authentic specimen distribution, while Ad can be deceived；The target of Ad is to distinguish the picture sample and true figure that Ag is generated as far as possible
Piece sample；The target of C be correctly classify as far as possible Ag generation picture, thereby realize wireless signal to the encrypted of picture
Journey；The target of Bg is to carry out fast decryption to encrypted signal pattern, and revert to original signal, the target of Bd as far as possible
It is the recovery degree distinguished the sample of signal of original normal signal sample and decryption as far as possible, and further increase Bg decryption,
Thereby realize the decrypting process of signal.
Training strategy in this way, the wireless signal more adjunction of the wireless signal encryption picture and Bg decryption that generate Ag
Nearly corresponding true value, such Ag can serve as a private cipher key of wireless signal, wireless signal are encrypted as picture；
Image data is translated as wireless signal data by another key that Bg can serve as wireless signal, and wireless by introducing
Modulation recognition MODEL C classifies to the wireless signal for using different modulating type to obtain, and can make wireless signal Encryption Model
Ag learns the characteristic of division attribute of wireless signal modulation type simultaneously in the training process, can be to different types of wireless signal
Preferably encrypted.
Specific experiment:
Data set basic condition includes: that (a) wireless signal data have 312000 training samples and 156000 test specimens
This, each sample-size is the matrix of 512*2, and sample value range is (- 6,6).Verifying collection is to take out from test sample at random
Take 10% sample size；(b) data set can be divided into 12 classes according to modulation type, every class equal part, there is every class in training set
26000 samples, every class has 13000 samples in test set；(c) state of signal-to-noise: for the 500*26*12=in test set
156000 samples, every one kind have 26 kinds of signal-to-noise ratio (- 20 to 30 all even numbers), different signal-to-noise ratio data collection in every class sample
Distribution is substantially are as follows: the sample that 500 signal-to-noise ratio are -20, the sample that 500 signal-to-noise ratio are -18, the sample that 500 signal-to-noise ratio are -16
This ... ... the sample that 500 signal-to-noise ratio are 28, the sample that 500 signal-to-noise ratio are 30.And for the data distribution feelings of training set
Condition is 1000 in the number of samples of one of classification signal-to-noise ratio, then the sample size of training set is 1000*26*12
=312000.(d) the simple reduction process divided by 6 has all been carried out to all wireless signal sample datas, has been input to facilitating
In model.
Model training system of the above-mentioned training set to above-mentioned building is trained, trained wireless signal encryption is obtained
Model and wireless signal decrypted model.And the sample in test set is input in wireless signal Encryption Model, it exports as such as Fig. 7
(a)~7 the corresponding coded signal image of 12 class wireless signals shown in (l), because the batch (batch) of input is 64, every part
Figure contains 64 figures, these pictures are manually difficult its content for being included respectively, illustrates this method adding for wireless signal
It is close to produce a desired effect.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of wireless signal encryption method based on antithesis GAN, comprising:
Using wireless signal Encryption Model by original wireless signal encryption at coded signal image；
Using wireless signal decrypted model by coded signal image decryption at decryption wireless signal；
The wireless signal Encryption Model and wireless signal decrypted model are obtained by the training of following model training system, the mould
Type training system includes:
Wireless signal refined net Ag, for encrypting to original wireless signal, input is original wireless signal, wireless communication
The decryption wireless signal of number decryption network Bg output, exports as coded signal image；
Wireless signal encryption differentiates network A d, and input is the encryption of normal color image, wireless signal refined net Ag output
Signal pattern is exported as the judgement to normal color image and coded signal image as a result, wherein being encrypted using wireless signal
When differentiating that the output feedback training wireless signal encryption of network A d differentiates network A d parameter, the category of normal color image is defined as
1, the category of coded signal image is defined as 0, in the output feedback training wireless communication for differentiating network A d using wireless signal encryption
When number refined net Ag parameter, coded signal image is used only, and the category of coded signal image is defined as 1 at this time；
Wireless signal decryption network Bg, for coded signal image to be decrypted, input is normal color image, wireless communication
The coded signal image of number refined net Ag output exports to decrypt wireless signal；
Wireless signal decryption differentiates network B d, and input is the decryption of original wireless signal, wireless signal decryption network Bg output
Wireless signal is exported as the judgement to original wireless signal and decryption wireless signal as a result, wherein being decrypted using wireless signal
When differentiating that the output feedback training wireless signal decryption of network B d differentiates network B d parameter, the category of original wireless signal is defined as
1, the category for decrypting wireless signal is defined as 0, in the output feedback training wireless communication for differentiating network B d using wireless signal decryption
When number decryption network Bg parameter, decryption wireless signal is used only, and the category for decrypting wireless signal at this time is defined as 1；
Wireless signal sorter network C, input are that the coded signal image of wireless signal refined net Ag output and generation should
The category of the wireless signal of coded signal image, the i.e. modulation type of wireless signal, export the classification results for coded signal, often
A classification results correspond to the modulation type of original wireless signal；
Training process are as follows:
Stage one: pre-training encrypts wireless signal using original wireless signal and corresponding wireless signal modulation type category
Network A g and wireless signal sorter network C carries out pre-training；
Stage two: retraining, firstly, fixed wireless signal encryption differentiates that network A d, wireless signal decryption differentiate network B d, wireless
The parameter of Modulation recognition network C, the parameter of joint training wireless signal refined net Ag and wireless signal decryption network Bg；So
Afterwards, the parameter of fixed wireless signal encryption network A g, wireless signal decryption network Bg, wireless signal sorter network C, joint training
Wireless signal encryption differentiates that network A d and wireless signal decryption differentiate the parameter of network B d；Finally, fixed wireless signal encryption is sentenced
The parameter of other network A d, wireless signal decryption network Bg, wireless signal decryption differentiation network B d, the encryption of joint training wireless signal
The parameter of network A g and wireless signal sorter network C；By above three joint training, until wireless signal refined net Ag and
Wireless signal encryption differentiate network A d, wireless signal decryption network Bg and wireless signal decryption differentiate network B d realization receive it is assorted
Weighing apparatus, training are completed, and trained wireless signal refined net Ag is wireless signal Encryption Model, trained wireless signal decryption
Network B g is wireless signal decrypted model.
2. as described in claim 1 based on the wireless signal encryption method of antithesis GAN, which is characterized in that the wireless signal
The structure of refined net includes:
The wireless signal being originally inputted is having a size of [512,2], wherein 512 indicate the sampling time point of wireless signal, 2 indicate wireless
The characteristic value of signal each time point, training process using the training of most small quantities of gradient descent method, it is most small quantities of in per a batch of
Wireless signal data sample number is generally taken as 64, and the characteristic layer having a size of [512,128] is obtained after LSTM unit,
In 512 correspond to original time point, the feature vector that 128 corresponding each time points are calculated obtains ruler using full connection
The very little characteristic layer for being 128 is obtained the characteristic layer having a size of 128 using full connection, is obtained using full articulamentum having a size of 64*64*3
Characteristic layer, this feature layer is deformed using reshape function to obtain the characteristic layer having a size of [64,64,3], wherein 64
Respectively correspond the length and width of characteristic layer, the depth of 3 character pair layers, using having a size of [5,5,64] convolution module and having a size of
The maximum pond module that [2,2] step-length is 2 obtains the characteristic layer having a size of [32,32,64], uses the convolution mould of [5,5,128]
Block and having a size of [2,2] step-length be 2 maximum pond module obtain the characteristic layer having a size of [16,16,128], using having a size of
[5,5,256] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the spy having a size of [8,8,256]
Layer is levied, the maximum pond module for being 2 using the convolution module having a size of [5,5,512] and having a size of [2,2] step-length obtains size
For the characteristic layer of [4,4,512], the maximum pond for being 2 using the convolution module having a size of [5,5,512] and having a size of [2,2] step-length
Change module and obtain the characteristic layer having a size of [2,2,512], using the convolution module having a size of [5,5,512] and having a size of [2,2]
The maximum pond module that step-length is 2 obtains the characteristic layer having a size of [1,1,512], uses the deconvolution having a size of [5,5,512]
Module obtains the characteristic layer having a size of [2,2,512], the characteristic layer of [2,2,512] before combining, obtain having a size of [2,2,
1024] characteristic layer uses the warp volume module of [5,5,512] to obtain the characteristic layer of [4,4,512], before combining [4,4,
512] characteristic layer obtains the characteristic layer of [4,4,1024], and the warp volume module of [5,5,256] is used to obtain [8,8,256]
Characteristic layer, the characteristic layer of [8,8,256] before combining obtain the characteristic layer of [8,8,512], use the warp of [5,5,128]
Volume module obtains the characteristic layer of [16,16,128], and the characteristic layer of [16,16,128] before combining obtains [16,16,256]
Characteristic layer uses the warp volume module of [5,5,64] to obtain the characteristic layer of [32,32,64], [32,32,64] before combining
Characteristic layer obtains the characteristic layer of [32,32,128], and the warp volume module of [5,5,3] is used to obtain the characteristic layer of [64,64,3],
What is obtained is the picture of corresponding ImageNet64, and picture a length of 64, width 64 includes RGB triple channel.
3. as described in claim 1 based on the wireless signal encryption method of antithesis GAN, which is characterized in that the wireless signal
The structure of decryption network Bg includes:
The picture size being originally inputted is [64,64,3], using the convolution module having a size of [5,5,64] and having a size of [2,2] step
A length of 2 maximum pond module obtains the characteristic layer having a size of [32,32,64], uses the convolution module having a size of [5,5,128]
With having a size of [2,2] step-length be 2 maximum pond module obtain the characteristic layer having a size of [16,16,128], using having a size of [5,
5,256] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the feature having a size of [8,8,256]
Layer, using having a size of [5,5,1024] convolution module and having a size of [2,2] step-length be 2 maximum pond module obtain having a size of
The characteristic layer of [4,4,1024], the maximum pond for being 2 using the convolution module having a size of [5,5,2048] and having a size of [2,2] step-length
Change module obtain the characteristic layer having a size of [2,2,2048], using having a size of [5,5,4096] convolution module and having a size of [2,
2] step-length be 2 maximum pond module obtain the characteristic layer having a size of [1, Isosorbide-5-Nitrae 096], using full link block obtain having a size of
The characteristic layer of 16384 (512*32) obtains the characteristic layer having a size of 4096 (512*8) using full link block, uses full connection
Module obtains the characteristic layer having a size of 1024 (512*2), its shape is become [512,2] using reshape function, obtains figure
Wireless signal corresponding to piece, wherein 512 corresponding sampling time points, the characteristic value of 2 corresponding each time points.
4. as described in claim 1 based on the wireless signal encryption method of antithesis GAN, which is characterized in that the wireless signal
Encryption differentiates that the structure of network A d includes:
The picture size of input is [64,64,3], uses the convolution module of [5,5,64] and is 2 most having a size of [2,2] step-length
Great Chiization module obtains the characteristic layer of [32,32,64], uses the convolution module of [5,5,128] and is 2 having a size of [2,2] step-length
Maximum pond module obtain the characteristic layer of [16,16,128], use the convolution module of [5,5,256] and having a size of [2,2] step
A length of 2 maximum pond module obtains the characteristic layer of [8,8,256], uses the convolution module of [5,5,512] and having a size of [2,2]
The maximum pond module that step-length is 2 obtains the characteristic layer of [4,4,512], uses the convolution module of [5,5,1] and having a size of [4,4]
The maximum pond module that step-length is 4 obtains the characteristic layer of [1,1,1], and obtained not normalized confidence value is identical with shape
0 or 1 matrix does cross entropy and calculates the coded signal image of Ag output and the gap of normal color image；
The wireless signal decryption differentiates that the structure of network B d includes:
The wireless signal sample-size being originally inputted is [512,2], and the spy having a size of [512,64] is obtained after LSTM unit
Layer is levied, wherein 512 correspond to original time point, the feature vector that 64 corresponding each time points are calculated uses full connection
Obtain the characteristic layer having a size of 64, obtain the characteristic layer having a size of 1 using full connection, obtained not normalized confidence value with
0 or 1 does the gap that cross entropy calculates original wireless signal and decryption wireless signal that wireless signal refined net Ag is generated.
5. as described in claim 1 based on the wireless signal encryption method of antithesis GAN, which is characterized in that the wireless signal
The structure of sorter network C includes:
The dimension of picture of input is [64,64,3], is 2 using the convolution kernel having a size of [5,5,64] and having a size of [2,2] step-length
Maximum pond module obtain the characteristic layer having a size of [32,32,64], use convolution kernel and size having a size of [5,5,128]
The maximum pond module for being 2 for [2,2] step-length obtains the characteristic layer having a size of [16,16,128], using having a size of [5,5,256]
Convolution kernel and having a size of [2,2] step-length be 2 maximum pond module obtain using ruler having a size of the characteristic layer of [8,8,256]
It is very little be the convolution kernel of [5,5,512] and be 2 having a size of [2,2] step-length maximum pond module obtain having a size of [4,4,512]
Characteristic layer obtains the characteristic layer having a size of 1024 using full connection, use full articulamentum obtain having a size of 12 characteristic layer as
Export (being directed to 12 classification tasks comprising 12 kinds of wireless signal modulation types), obtained not normalized confidence value with
The category of the corresponding original wireless signal of coded signal image does cross entropy and calculates distance.
6. as described in claim 1 based on the wireless signal encryption method of antithesis GAN, which is characterized in that pre-training it is specific
Process are as follows:
Fixed wireless signal encryption differentiates that network A d, wireless signal decryption differentiate network B d's and wireless signal decryption network Bg
Trained epochs=N1 is arranged in parameter, i.e. training dataset is used N1 times；The input of wireless signal refined net Ag is original
Beginning wireless signal exports as encrypted coded signal image, and the input of wireless signal sorter network C is wireless signal densification network
The coded signal image of network Ag output, exports as the class to the debug-type of original wireless signal corresponding to coded signal image
Mark prediction；Ag-C pairs of disaggregated model as a whole of wireless signal refined net Ag and wireless signal sorter network C at this time
The modulation type of wireless signal is classified, and wireless signal refined net Ag is equivalent to the feature extraction mould of disaggregated model Ag-C
Block, wireless signal sorter network C are equivalent to the categorization module of disaggregated model Ag-C.
7. as claimed in claim 6 based on the wireless signal encryption method of antithesis GAN, which is characterized in that retraining it is specific
Process are as follows:
(1) fixed wireless signal encryption differentiates that network A d, wireless signal decryption differentiate network B d, wireless signal sorter network C
Parameter, by original wireless signal xsignal, wireless signal decryption network Bg output decryption wireless signal Bg (ximage) as wireless
The input of signal encryption network A g, the parameter of training Ag, the coded signal image for exporting it are more nearly normal color image；
And by normal color image ximage, wireless signal refined net Ag output coded signal image Ag (xsignal) it is used as wireless communication
The input of number decryption network Bg, the parameter of training Bg, the decryption wireless signal for exporting it are more nearly original wireless signal, with
This realizes the decrypting process of encrypted wireless signal；
(2) parameter of fixed wireless signal encryption network A g, wireless signal decryption network Bg, wireless signal sorter network C, by nothing
The coded signal image Ag (x of line signal encryption network A g outputsignal) and normal color imageBlended data, as
Wireless signal encryption differentiates the input of network A d, and the parameter of training Ad can distinguish coded signal image and normal color
Image；And the decryption wireless signal Bg (x for exporting wireless signal decryption network Bgimage) and original wireless signalIt is mixed
Data are closed, the input for differentiating network B d is decrypted as wireless signal, the parameter of training Bd can distinguish decryption wireless signal
With original wireless signal；
(3) fixed wireless signal encryption differentiates that network A d, wireless signal decryption network Bg, wireless signal decryption differentiate network B d's
Parameter, by original wireless signal xsignal, wireless signal decryption network Bg output decryption wireless signal Bg (ximage) as wireless
The input of signal encryption network A g, and the coded signal image Ag (x that wireless signal refined net Ag is exportedsignal) and it is corresponding
Input of the true category y as wireless signal sorter network C, coorinated training wireless signal refined net Ag and wireless signal point
The parameter of class network C；
(4) step (1)~(3) are repeated, until wireless signal refined net Ag and wireless signal encryption differentiate network A d, wireless communication
Number decryption network Bg and wireless signal decryption differentiate that network B d realizes Nash Equilibrium, i.e. wireless signal refined net Ag and radio-type
Number encryption differentiates that both network A d game tends to balance, and wireless signal decryption network Bg and wireless signal decryption differentiate network B d two
The game of person tends to balance, and training cut-off, trained wireless signal refined net Ag is wireless signal Encryption Model, trains
Wireless signal decryption network Bg be wireless signal decrypted model.
8. as claimed in claim 7 based on the wireless signal encryption method of antithesis GAN, which is characterized in that when realization receive it is assorted
Have when weighing apparatus:
Wherein, xAgIndicate the coded signal image of wireless signal refined net Ag output, xBgIndicate wireless signal decryption network Bg
The decryption wireless signal of output, xAg~pAg indicates xAgSample the output from wireless signal refined net Ag, xBg~pBg indicates xBg
The output from wireless signal decryption network Bg is sampled, pAg and pBg respectively indicate the probability distribution of Ag and Bg output, Ad (xAg) table
Show that wireless signal encryption differentiates network A d to xAgDifferentiation probability, Bd (xBg) indicate that wireless signal decryption differentiates network B d to xBg
Differentiation probability, E () indicate cross entropy expectation；Pdata indicates raw data set,Indicate normal signal
SampleFrom original signal data Pdata；Indicate normal image dataFrom original graph
As data Pdata；Y indicates the modulation type category of original wireless signal,Indicate normal wireless sample of signalFrom wireless signal data set, C () indicates wireless signal sorter network C to the classification knot of wireless signal modulation type for sampling
Fruit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811268894.7A CN109413068B (en) | 2018-10-29 | 2018-10-29 | Wireless signal encryption method based on dual GAN |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811268894.7A CN109413068B (en) | 2018-10-29 | 2018-10-29 | Wireless signal encryption method based on dual GAN |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109413068A true CN109413068A (en) | 2019-03-01 |
| CN109413068B CN109413068B (en) | 2021-01-26 |
Family
ID=65469565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811268894.7A Active CN109413068B (en) | 2018-10-29 | 2018-10-29 | Wireless signal encryption method based on dual GAN |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109413068B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111047006A (en) * | 2019-10-28 | 2020-04-21 | 浙江工业大学 | Anti-attack defense model based on dual-generation network and application |
| CN111654368A (en) * | 2020-06-03 | 2020-09-11 | 电子科技大学 | Key generation method for generating countermeasure network based on deep learning |
| CN114710371A (en) * | 2022-06-08 | 2022-07-05 | 深圳市乐凡信息科技有限公司 | Method, device, equipment and storage medium for safely signing electronic data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951919A (en) * | 2017-03-02 | 2017-07-14 | 浙江工业大学 | A kind of flow monitoring implementation method based on confrontation generation network |
| CN107220600A (en) * | 2017-05-17 | 2017-09-29 | 清华大学深圳研究生院 | A kind of Picture Generation Method and generation confrontation network based on deep learning |
| CN108573479A (en) * | 2018-04-16 | 2018-09-25 | 西安电子科技大学 | The facial image deblurring and restoration methods of confrontation type network are generated based on antithesis |
-
2018
- 2018-10-29 CN CN201811268894.7A patent/CN109413068B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951919A (en) * | 2017-03-02 | 2017-07-14 | 浙江工业大学 | A kind of flow monitoring implementation method based on confrontation generation network |
| CN107220600A (en) * | 2017-05-17 | 2017-09-29 | 清华大学深圳研究生院 | A kind of Picture Generation Method and generation confrontation network based on deep learning |
| CN108573479A (en) * | 2018-04-16 | 2018-09-25 | 西安电子科技大学 | The facial image deblurring and restoration methods of confrontation type network are generated based on antithesis |
Non-Patent Citations (2)
| Title |
|---|
| SURYAWANSHI S B: "A triple-key Chaotic neural network for", 《 INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES& EMERGING TECHNOLOGIES》 * |
| 王万良: "生成式对抗网络研究进展", 《通信学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111047006A (en) * | 2019-10-28 | 2020-04-21 | 浙江工业大学 | Anti-attack defense model based on dual-generation network and application |
| CN111047006B (en) * | 2019-10-28 | 2023-04-21 | 浙江工业大学 | Dual generation network-based anti-attack defense model and application |
| CN111654368A (en) * | 2020-06-03 | 2020-09-11 | 电子科技大学 | Key generation method for generating countermeasure network based on deep learning |
| CN111654368B (en) * | 2020-06-03 | 2021-10-08 | 电子科技大学 | Key generation method for generating countermeasure network based on deep learning |
| CN114710371A (en) * | 2022-06-08 | 2022-07-05 | 深圳市乐凡信息科技有限公司 | Method, device, equipment and storage medium for safely signing electronic data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109413068B (en) | 2021-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zheng et al. | Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification | |
| Kong et al. | Detect and locate: Exposing face manipulation by semantic-and noise-level telltales | |
| CN109413068A (en) | A kind of wireless signal encryption method based on antithesis GAN | |
| Guo et al. | Self-supervised GANs with similarity loss for remote sensing image scene classification | |
| CN107341463A (en) | A kind of face characteristic recognition methods of combination image quality analysis and metric learning | |
| Qian et al. | Spot evasion attacks: Adversarial examples for license plate recognition systems with convolutional neural networks | |
| CN109831422A (en) | A kind of encryption traffic classification method based on end-to-end sequence network | |
| CN109800785A (en) | One kind is based on the relevant data classification method of expression and device certainly | |
| CN109948692A (en) | Picture detection method is generated based on the computer of multiple color spaces convolutional neural networks and random forest | |
| CN113553624A (en) | WGAN-GP privacy protection system and method based on improved PATE | |
| CN109887047A (en) | A kind of signal-image interpretation method based on production confrontation network | |
| Luo et al. | Beyond universal attack detection for continuous-variable quantum key distribution via deep learning | |
| William et al. | Implementation of hand written based signature verification technology using deep learning approach | |
| CN114499861A (en) | Quantum key cloud security situation sensing method based on machine learning | |
| Neagoe et al. | CNN hyperspectral image classification using training sample augmentation with generative adversarial networks | |
| CN108090905B (en) | The judgment method and system of producing line exception | |
| Kumar et al. | Secured Cloud Application for Detection of Brain Tumor using Deep Learning Algorithms | |
| Liu et al. | MSNet: A multiple supervision network for remote sensing scene classification | |
| CN109951278A (en) | A kind of asymmetrical digital image encryption method based on generalized chaotic synchronization system | |
| CN115690484A (en) | Unattended content auditing method | |
| CN115618226A (en) | Encrypted traffic data synthesis method based on generation countermeasure network model | |
| Peng et al. | Counterfactual image enhancement for explanation of face swap deepfakes | |
| Le et al. | Generating high-fidelity cybersecurity data with generative adversarial networks | |
| Gadgilwar et al. | Exploring Deepfakes-Creation Techniques Detection Strategies and Emerging Challenges: A Survey | |
| Huang et al. | Ffaa: Multimodal large language model based explainable open-world face forgery analysis assistant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |