CN113726979B - Picture encryption method, picture decryption method, picture encryption system and related devices - Google Patents
Picture encryption method, picture decryption method, picture encryption system and related devices Download PDFInfo
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Abstract
The application provides a picture encryption method, which comprises the following steps: acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers; recording metadata of the picture to be encrypted when passing through each layer; and taking the metadata as encryption data of the picture to be encrypted. The application realizes controllable encryption process and is difficult to crack, and the obtained metadata can select and extract part of metadata as encryption data according to the information quantity, so that the obtained encryption data is far smaller than the original information. The application also provides a picture decryption method, a picture encryption system, a computer readable storage medium and electronic equipment, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of data encryption, and in particular, to a picture encryption method, a picture decryption method, an encryption system, and related devices.
Background
At present, encryption algorithms are divided into symmetric encryption and asymmetric encryption, wherein the encryption of the symmetric encryption algorithm is the same as the decryption key, the encryption key of the asymmetric encryption algorithm is different from the decryption key, and a hash algorithm without a key is also available.
However, the key management of the symmetric algorithm is difficult, and the symmetric algorithm is not suitable for the Internet and is generally used for an internal system; the safety is a middle gear; the encryption speed is high, the software encryption and decryption speed is at least 100 times faster, and the method is suitable for encryption and decryption of large data volume. The key management of the asymmetric algorithm is easy, the encryption performance is strong, the security is high, but the encryption speed is relatively slow, the method is suitable for encrypting and decrypting small data volume or signing data, and the speed is very slow when processing large data volume. However, whether the encryption is symmetric or asymmetric, the data after encryption is 2-3 times larger than the original data, and great difficulty is brought to the data storage after encryption.
Disclosure of Invention
The application aims to provide a picture encryption method, a picture decryption method, a picture encryption system, a computer readable storage medium and electronic equipment, which can reduce the data size of encrypted information.
In order to solve the technical problems, the application provides a picture encryption method, which comprises the following specific technical scheme:
Acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers;
recording metadata of the picture to be encrypted when passing through each layer;
And taking the metadata as encryption data of the picture to be encrypted.
Optionally, the method further comprises:
obtaining a pre-training model of the DNN classification model from a PyTorch library;
And retraining the last two layers of the pre-training model to obtain the DNN classification model.
The application also provides a picture decryption method based on the encrypted data of the scheme, which comprises the following steps:
acquiring encryption data, and taking a noise image or a solid-color image as an initial input image of a DNN classification model;
taking the encrypted data as supervision data, and determining a metadata extraction strategy of the encrypted data;
extracting total loss corresponding to the metadata extraction strategy; the metadata extraction strategy comprises an extraction layer number and a target extraction layer;
And reversely deriving the total loss by using a BP algorithm to obtain a decrypted picture corresponding to the encrypted data.
Optionally, extracting the total loss corresponding to the metadata extraction policy includes:
determining a target extraction layer in the metadata extraction strategy;
Determining the weight of each target extraction layer;
Determining the loss of each target extraction layer according to a preset loss function and the weight;
and calculating the total loss of the encrypted data according to the loss of each target extraction layer.
Optionally, after obtaining the decrypted picture corresponding to the encrypted data, the method further includes:
and taking the decrypted picture as the initial input image, carrying out iteration update by using an optimizer, stopping iteration when the iteration times meet a preset value, and outputting a cyclic decrypted picture.
Optionally, performing the iterative updating with the optimizer includes:
And reversely deriving the total loss, and iteratively updating the reversely deriving by using the optimizer.
Optionally, the preset loss function is a euclidean distance or KL divergence.
The application also provides a picture encryption system, which comprises:
The picture acquisition module is used for acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers;
the data recording module is used for recording metadata when the picture to be encrypted passes through each layer;
And the encryption data module is used for taking the metadata as the encryption data of the picture to be encrypted.
The application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method as described above.
The application also provides an electronic device comprising a memory in which a computer program is stored and a processor which when calling the computer program in the memory implements the steps of the method as described above.
The application provides a picture encryption method, which comprises the following steps: acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers; recording metadata of the picture to be encrypted when passing through each layer; and taking the metadata as encryption data of the picture to be encrypted.
The application adopts the DNN classification model to encrypt the picture to be encrypted, inputs the picture to be encrypted into the DNN classification model, carries out forward reasoning to obtain metadata when the picture to be encrypted passes through each layer of the DNN model and is used as encryption data, and the encryption process is controllable and difficult to crack because the encryption process is related to the DNN classification model, model parameters and metadata extraction modes, and the obtained metadata can be used for selecting and extracting partial metadata as encryption data according to the information quantity, so that the obtained encryption data is far smaller than the original information.
The application also provides a picture decryption method, a picture encryption system, a computer readable storage medium and electronic equipment, which have the beneficial effects and are not repeated here.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a picture encryption method according to an embodiment of the present application;
Fig. 2 is a flowchart of a picture decryption method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a picture encryption system according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a picture encryption method according to an embodiment of the present application, and the specific technical scheme of the present application is as follows:
S101: acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model;
S102: recording metadata of the picture to be encrypted when passing through each layer;
S103: and taking the metadata as encryption data of the picture to be encrypted.
The present embodiment defaults to the DNN classification model having been obtained or acquired before step S101 is performed. The method for obtaining the DNN classification model is not particularly limited, and the DNN classification model may be directly obtained from a PyTorch library (a deep learning library), or a pre-training model of the DNN classification model may be obtained from a PyTorch library, and then the last two layers of the pre-training model are retrained to obtain the DNN classification model. The image Net pre-training model can be directly used, then the model is finely adjusted, the required calculation amount is small, the operation is only needed once, and the subsequent repeated use can be realized.
It should be noted that the DNN classification model comprises several layers, but the first layer is the input layer, the last layer is the output layer, and the middle comprises hidden layers of several layers. Inputting the picture to be encrypted into a DNN classification model, firstly carrying out forward reasoning, recording metadata reaching each layer of the picture to be encrypted when the picture passes through each layer of the DNN classification model, and storing the metadata as encryption data. The extraction of metadata may have various policies, such as any layer, any number of layers, etc. The encryption party transmits the metadata to the decryption party. It is easy to understand that the metadata of each layer except the input layer can be recorded, but the encryption level can be freely set by a person skilled in the art, so as to determine the layer corresponding to the metadata to be extracted and the number of layers, in other words, only the metadata of at least one hidden layer needs to be included in the encryption process.
In addition, in step S102, metadata needs to be recorded when the picture to be encrypted passes through each layer, but in step S103, all metadata need not be used as encrypted data, but metadata of at least one layer in the hidden layers may be set and selected by a person skilled in the art as encrypted data.
When the picture to be encrypted passes through each layer in the DNN classification model, an input weighted sum is generally calculated, and an activation function is called to calculate the output of the layer as metadata.
According to the embodiment of the application, the DNN classification model is adopted to encrypt the picture to be encrypted, the picture to be encrypted is input into the DNN classification model, forward reasoning is carried out, metadata of the picture to be encrypted when passing through each layer of the DNN model is obtained and is used as encryption data, the encryption process is controllable and difficult to crack due to the fact that the encryption process is related to the DNN classification model, model parameters and metadata extraction modes, and the obtained metadata can be used as encryption data by selecting and extracting part of metadata according to information quantity, so that the obtained encryption data is far smaller than original information.
Referring to fig. 2, fig. 2 is a flowchart of a picture decryption method according to an embodiment of the present application, and the present application further provides a picture decryption method, for decrypting encrypted data according to the above scheme, where the scheme includes:
S201: acquiring encryption data, and taking a noise image or a solid-color image as an initial input image of a DNN classification model;
The encrypted data obtained in this step refers to the encrypted data output in the previous embodiment, and in this step, the noise map or the solid-color map is first input into the DNN classification model as an initial input image, and then metadata is extracted and loss is calculated, and then the original picture to be encrypted is restored gradually on the basis of the noise map or the solid-color map.
S202: taking the encrypted data as supervision data, and determining a metadata extraction strategy of the encrypted data;
in this step, it is necessary to take the encrypted data as the supervision data and determine the metadata extraction policy of the encrypted data. The metadata extraction policy includes an extraction layer number and a target extraction layer. It should be noted that the metadata extraction layer should be slightly data existing in the encrypted data. For example, if the hidden layer includes five layers, the encrypted data is actually the data of the first three layers of the hidden layer of the encryption process, and the determined metadata extraction policy should also be within the range of the first three layers. In other words, the number of extraction layers in the metadata extraction policy should be no greater than the number of layers contained in the encrypted data. Naturally, along with different metadata extraction strategies, namely different extracted metadata layers, the restoration degree of the obtained decrypted picture also has corresponding difference.
S203: extracting total loss corresponding to the metadata extraction strategy;
after determining the metadata extraction strategy, the total loss can be calculated, and specifically, the following steps can be adopted:
S2031: determining a target extraction layer in the metadata extraction strategy;
S2032: determining the weight of each target extraction layer;
S2033: determining the loss of each target extraction layer according to a preset loss function and the weight;
s2034: and calculating the total loss of the encrypted data according to the loss of each target extraction layer.
Firstly, determining a target extraction layer, definitely needing to extract metadata of which layer, determining corresponding weight of the metadata of the layer, and then determining the loss of the target extraction layer according to a preset loss function and weight. The predetermined loss function may be a euclidean distance, a KL divergence, or other functions, or may be any function that sufficiently describes a difference between two sets of data, which is not limited herein. Meanwhile, because the depth of each layer in the model is different, the information extracted by different layers is different, and the information extracting capability is also different, a certain weight can be set for the loss of each target extraction layer, so that decryption reproduction can be realized.
In other words, the objective of this step is to obtain the total loss from the difference between the mean, variance and metadata of the initial input image arriving at each layer in the forward calculation, and integrate the total loss with a preset loss function.
S204: and reversely deriving the total loss by using a BP algorithm to obtain a decrypted picture corresponding to the encrypted data.
After the total loss is obtained, a Back Propagation algorithm (BP) can be used to reversely derive the total loss, then reversely derive the total loss, and the reverse derivation is iteratively updated by using an optimizer, so that the initial input image is updated based on the derivative, and thus, one reproduction of the initial input image is realized, and the single reproduction mainly can be performed in some detail aspects, and the input image can be continuously and iteratively updated.
For example, after obtaining the decrypted picture corresponding to the encrypted data, the decrypted picture may be further used as the initial input image, and an optimizer may be used to perform iteration update, and stop iteration when the iteration number meets a preset value, and output a loop decrypted picture. Therefore, the reproduction is from point to surface, the whole loop iteration process can observe that the weight of each layer in the DNN classification model can be controlled, or the total loop times can be controlled, so that the controllable reproduction step is realized, and the reproduction can be explained. And the current model analysis theory based on AI is not breakable, so that the data security is ensured. The optimization function employed by the optimizer is not particularly limited herein. On the basis of the embodiment, different loss functions or optimization functions can be adopted, and the scheme can be realized.
The above decryption process can be seen that the disclosed picture decryption method is realized by the limestone, and the input image is reproduced by BP reverse derivation, so that the combination of controllable, interpretable, high-compression-ratio and indestructible information encryption and decryption is realized, the security of data encryption and decryption is ensured, and meanwhile, the decryption process can be observed in a whole flow, and the decryption state is convenient to view in real time.
The following describes a picture encryption system provided by an embodiment of the present application, and the picture encryption system described below and the picture encryption method described above may be referred to correspondingly.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a picture encryption system according to an embodiment of the present application, and the present application further provides a picture encryption system, including:
The picture acquisition module is used for acquiring a picture to be encrypted, and performing forward calculation on the picture to be encrypted by using a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers;
the data recording module is used for recording metadata when the picture to be encrypted passes through each layer;
And the encryption data module is used for taking the metadata as the encryption data of the picture to be encrypted.
Based on the above embodiment, as a preferred embodiment, further comprising:
The model acquisition module is used for acquiring a pre-training model of the DNN classification model from the PyTorch library; and retraining the last two layers of the pre-training model to obtain the DNN classification model.
The following describes a picture decryption system according to an embodiment of the present application, and the picture decryption system described below and the picture decryption method described above may be referred to correspondingly. The application also provides a picture decryption system, which comprises:
the data acquisition module is used for acquiring encrypted data, and taking a noise image or a pure color image as an initial input image of the DNN classification model;
The strategy determining module is used for taking the encrypted data as supervision data and determining a metadata extraction strategy of the encrypted data;
The loss calculation module is used for extracting the total loss corresponding to the metadata extraction strategy; the metadata extraction strategy comprises an extraction layer number and a target extraction layer;
and the decryption module is used for carrying out reverse derivation on the total loss by utilizing a BP algorithm to obtain a decryption picture corresponding to the encrypted data.
Based on the above embodiments, as a preferred embodiment, the loss calculation module includes:
The layer number determining unit is used for determining a target extraction layer in the metadata extraction strategy;
A weight determining unit configured to determine a weight of each of the target extraction layers;
A layer loss determining unit, configured to determine a loss of each target extraction layer according to a preset loss function and the weight;
And a total loss determination unit for calculating the total loss of the encrypted data according to the loss of each target extraction layer.
Based on the above embodiment, as a preferred embodiment, further comprising:
And the iteration updating module is used for taking the decrypted picture as the initial input image, carrying out iteration updating by using an optimizer, stopping iteration when the iteration times meet a preset value, and outputting a cyclic decrypted picture.
Based on the above embodiments, as a preferred embodiment, the iterative updating module includes:
And the optimizer unit is used for reversely deriving the total loss and iteratively updating the reversely derived value by utilizing the optimizer.
Based on the above embodiment, as a preferred embodiment, the preset loss function is a euclidean distance or KL divergence.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed, performs the steps provided by the above-described embodiments. The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The application also provides an electronic device, which can comprise a memory and a processor, wherein the memory stores a computer program, and the processor can realize the steps provided by the embodiment when calling the computer program in the memory. Of course the electronic device may also include various network interfaces, power supplies, etc.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. The system provided by the embodiment is relatively simple to describe as it corresponds to the method provided by the embodiment, and the relevant points are referred to in the description of the method section.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present application and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the application can be made without departing from the principles of the application and these modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (8)
1. A picture decryption method, comprising:
Acquiring encryption data, and performing forward calculation by taking a noise image or a solid-color image as an initial input image of a DNN classification model; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers; the encryption data are metadata when the picture to be encrypted passes through each layer when forward computation is carried out on the picture to be encrypted by utilizing the DNN classification model;
taking the encrypted data as supervision data, and determining a metadata extraction strategy of the encrypted data;
extracting total loss corresponding to the metadata extraction strategy; the metadata extraction strategy comprises an extraction layer number and a target extraction layer;
Performing reverse derivation on the total loss by using a BP algorithm to obtain a decrypted picture corresponding to the encrypted data;
wherein, extracting the total loss corresponding to the metadata extraction policy includes:
determining a target extraction layer in the metadata extraction strategy;
Determining the weight of each target extraction layer;
Determining the loss of each target extraction layer according to a preset loss function and the weight;
calculating the total loss of the encrypted data according to the loss of each target extraction layer;
Wherein the determining the loss of each target extraction layer according to the preset loss function and the weight includes:
And determining the loss of each target extraction layer according to a preset loss function and the weight based on the difference value between the target value obtained by the initial input image reaching each target extraction layer and the metadata of the layer.
2. The picture decryption method according to claim 1, further comprising:
obtaining a pre-training model of the DNN classification model from a PyTorch library;
And retraining the last two layers of the pre-training model to obtain the DNN classification model.
3. The picture decryption method according to claim 1, wherein after obtaining the decrypted picture corresponding to the encrypted data, further comprising:
and taking the decrypted picture as the initial input image, carrying out iteration update by using an optimizer, stopping iteration when the iteration times meet a preset value, and outputting a cyclic decrypted picture.
4. A picture decryption method according to claim 3, wherein iteratively updating with an optimizer comprises:
And reversely deriving the total loss, and iteratively updating the reversely deriving by using the optimizer.
5. The picture decryption method according to claim 1, wherein the predetermined loss function is a euclidean distance or a KL divergence.
6. A picture decryption system, comprising:
The data acquisition module is used for acquiring encrypted data, and taking a noise image or a pure-color image as an initial input image of the DNN classification model to perform forward calculation; the DNN classification model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer comprises a plurality of intermediate layers; the encryption data are metadata when the picture to be encrypted passes through each layer when forward computation is carried out on the picture to be encrypted by utilizing the DNN classification model;
The strategy determining module is used for taking the encrypted data as supervision data and determining a metadata extraction strategy of the encrypted data;
The loss calculation module is used for extracting the total loss corresponding to the metadata extraction strategy; the metadata extraction strategy comprises an extraction layer number and a target extraction layer;
The decryption module is used for carrying out reverse derivation on the total loss by utilizing a BP algorithm to obtain a decryption picture corresponding to the encrypted data;
wherein, the loss calculation module includes:
The layer number determining unit is used for determining a target extraction layer in the metadata extraction strategy;
A weight determining unit configured to determine a weight of each of the target extraction layers;
A layer loss determining unit, configured to determine a loss of each target extraction layer according to a preset loss function and the weight;
a total loss determination unit configured to calculate a total loss of the encrypted data based on the loss of each of the target extraction layers;
The layer loss determining unit is specifically configured to determine a loss of each target extraction layer according to a preset loss function and the weight, based on a difference between a target value obtained by the initial input image reaching each target extraction layer and the metadata of the layer.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-5.
8. An electronic device comprising a memory in which a computer program is stored and a processor that, when calling the computer program in the memory, performs the steps of the method according to any of claims 1-5.
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