CN117574404A - Image security processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses an image security processing method, device, equipment and storage medium, comprising the following steps: acquiring an original image input by a user, and establishing a blank image according to the original image; copying target metadata in an original image into a blank image, performing random bit operation on pixel values of each pixel point in the original image, and copying updated pixel values corresponding to each pixel point into the blank image to obtain a target image; inputting the target image into a depth self-encoder, and processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image; and storing the coding vector corresponding to the target image in a server. The technical scheme of the embodiment of the invention can improve the safety processing efficiency of the image and ensure the safety of the image data entering the server.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for image security processing.
Background
The image security process is a process of processing an image file in order to prevent an attack means such as malicious code from being included in the image file uploaded to the server by a user. Common ways to write malicious code into an image file are mainly writing image metadata, appending at the end of the image, steganographically to image pixels, and so on.
The existing image security processing mode is generally as follows: obtaining a target image according to a preset image resolution, dividing the target image according to a preset image dividing rule to obtain a key information area and a non-key information area, encrypting the key information area based on a pixel color difference algorithm to obtain an encrypted key information area, and respectively compressing the encrypted key information area and the non-key information area to obtain a compressed target image.
However, when the conventional processing method encrypts the key region of the image based on the pixel color difference algorithm, multiple bit operations are required for each pixel point in the image, and the operation rule of each pixel point is different. In practical use, for larger-sized images, the computation time increases by a multiple of square, resulting in a longer time-consuming security process. Therefore, there is a need to propose a new image security processing method to improve image processing performance.
Disclosure of Invention
The invention provides an image security processing method, an image security processing device, image security processing equipment and a storage medium, which can improve the security processing efficiency of images and ensure the security of image data entering a server.
According to an aspect of the present invention, there is provided an image security processing method, the method including:
acquiring an original image input by a user, and establishing a blank image according to the original image;
copying target metadata in the original image into a blank image, performing random bit operation on pixel values of each pixel point in the original image, and copying updated pixel values corresponding to each pixel point into the blank image to obtain a target image;
inputting the target image into a depth self-encoder, and processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image;
and storing the coding vector corresponding to the target image in a server.
Optionally, performing random bit operation on a pixel value of each pixel point in the original image includes:
acquiring an RGB value of each pixel point in the original image, and randomly extracting a target bit value from the RGB value of each pixel point according to a preset bit number;
and inverting the target bit number value corresponding to each pixel point.
Optionally, before acquiring an original image input by a user and establishing a blank image according to the original image, the method further includes:
constructing a depth self-encoder, and inputting image training samples to a current processing layer in the depth self-encoder;
training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer;
taking the output result of the current processing layer as input data of a subsequent processing layer, and training to obtain the weight corresponding to the subsequent processing layer according to the output result of the subsequent processing layer;
and re-using the subsequent processing layer as the current processing layer, and returning to execute the operation of inputting the image training sample to the current processing layer in the depth self-encoder until the number of the processing layers in the depth self-encoder meets a preset threshold value.
Optionally, training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer includes:
determining a mean square error between the image training sample and an output result of the current processing layer;
and taking the mean square error as a loss function, and training according to the loss function to obtain the weight corresponding to the current processing layer.
Optionally, establishing a blank image according to the original image includes:
and acquiring a target size corresponding to the original image, and establishing a blank image with the same size as the original image according to the target size.
Optionally, after storing the encoding vector corresponding to the target image in the server, the method further includes:
responding to an image reading request triggered by a user, and acquiring a coding vector corresponding to the target image from the server;
and processing the coded vector through a decoding layer of the depth self-encoder to obtain a target image.
According to another aspect of the present invention, there is provided an image security processing apparatus including:
the image acquisition module is used for acquiring an original image input by a user and establishing a blank image according to the original image;
the image processing module is used for copying the target metadata in the original image into a blank image, carrying out random bit operation on the pixel value of each pixel point in the original image, and copying the updated pixel value corresponding to each pixel point into the blank image to obtain a target image;
the image coding module is used for inputting the target image into a depth self-encoder, and processing the target image through a coding layer of the depth self-encoder to obtain a coding vector corresponding to the target image;
and the image storage module is used for storing the coding vector corresponding to the target image in a server.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image security processing method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the image security processing method according to any one of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, the blank image is established according to the original image by acquiring the original image input by a user, the target metadata in the original image is copied into the blank image, the pixel value of each pixel point in the original image is subjected to random bit operation, the updated pixel value corresponding to each pixel point is copied into the blank image to obtain the target image, the target image is input into the depth self-encoder, the target image is processed through the coding layer of the depth self-encoder to obtain the coding vector corresponding to the target image, and the coding vector corresponding to the target image is stored in the server.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an image security processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another image security processing method provided according to an embodiment of the present invention;
FIG. 3 is a flow chart of another image security processing method provided according to an embodiment of the present invention;
fig. 4 is a schematic structural view of an image security processing apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing an image security processing method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of an image security processing method according to an embodiment of the present invention, where the method may be performed by an image security processing device, and the image security processing device may be implemented in hardware and/or software, and the image security processing device may be configured in an electronic device. As shown in fig. 1, the method includes:
step 110, acquiring an original image input by a user, and establishing a blank image according to the original image.
In this embodiment, the original image may be an initial image file uploaded by the user at the server. After the original image is obtained, optionally, a blank image corresponding to the original image may be created according to an aspect ratio of the original image.
And 120, copying the target metadata in the original image into a blank image, performing random bit operation on the pixel value of each pixel point in the original image, and copying the updated pixel value corresponding to each pixel point into the blank image to obtain the target image.
In this embodiment, after a blank image is created, important metadata (i.e., target metadata) such as a date and a shooting location in an original image may be copied into the blank image, then a random sampling algorithm is adopted to perform a random bit operation on a pixel value of each pixel point in the original image, and a result (i.e., updated pixel value) of each pixel point after the random bit operation is copied into the blank image, so as to obtain the target image.
The method has the advantages that the binary data of the original image can be modified under the condition of retaining the visual characteristics of the original image by carrying out random bit operation on the pixel value of each pixel point in the original image, so that the effect of destroying the potential malicious codes in the original image is achieved.
In a specific embodiment, the sampling interval corresponding to the random sampling algorithm may satisfy a uniform distribution or a normal distribution.
And 130, inputting the target image into a depth self-encoder, and processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image.
In this embodiment, the depth self-encoder is a neural network that uses a back-propagation algorithm to make the output value equal to the input value, which compresses the input into a potential spatial representation, and then reconstructs the output from this representation. Alternatively, the training process of the depth self-encoder may be implemented by using back propagation, or may be implemented by using an extreme learning algorithm, where both training adopts a layer-by-layer training strategy, which is not limited in this embodiment.
In this step, specifically, after the target image is input into the depth self-encoder, the target image may be encoded by the encoding layer in the depth self-encoder according to the weight trained in advance, so as to obtain the encoding vector corresponding to the target image.
And 140, storing the coding vector corresponding to the target image in a server.
In this embodiment, the encoding vector may be used as a storage object corresponding to the original image, and the storage object may be stored in a preset location of the server.
In this embodiment, by creating a blank image corresponding to the original image, copying the target metadata in the original image into the blank image, and discarding other metadata in the original image, malicious codes that may exist in the original image may be prevented; secondly, the image processing efficiency can be improved by carrying out random bit operation on the pixel value of each pixel point in the original image, the binary data of the image can be modified in a shorter time, and the effect of preliminarily cleaning malicious codes which can be hidden in the image can be achieved; finally, the depth self-encoder is used for encoding and storing the image, so that the image data can be compressed, the storage cost is reduced, the image can be prevented from being attacked again, and the safety of the image data entering the server is improved.
According to the technical scheme provided by the embodiment of the invention, the blank image is established according to the original image by acquiring the original image input by a user, the target metadata in the original image is copied into the blank image, the pixel value of each pixel point in the original image is subjected to random bit operation, the updated pixel value corresponding to each pixel point is copied into the blank image to obtain the target image, the target image is input into the depth self-encoder, the target image is processed through the coding layer of the depth self-encoder to obtain the coding vector corresponding to the target image, and the coding vector corresponding to the target image is stored in the server.
Fig. 2 is a flowchart of another image security processing method according to an embodiment of the present invention, as shown in fig. 2, where the method includes:
step 210, acquiring an original image input by a user and a target size corresponding to the original image, and establishing a blank image consistent with the original image in size according to the target size.
And 220, copying the target metadata in the original image into a blank image.
Step 230, obtaining an RGB value of each pixel point in the original image, randomly extracting a target bit value from the RGB values of each pixel point according to a preset bit number, and inverting the target bit value corresponding to each pixel point.
In this step, specifically, a random decision strategy may be adopted to invert several bits in the low n (0 n 8) bits of RGB values of each pixel point in the original image. In order to preserve the color characteristics of the original image, the value of n should be small, for example, when n=3, the result of the bit operation is not noticeable to the naked eye.
In practical applications, most of the pixel colors of the image are represented by 8-bit RGB values, ranging from 0,255, and the noise generated when the low bits are operated is small. For example, after the 2 nd low order bits of 11011011001 are inverted, 11011011 can be obtained, the RGB value is increased by 2 in only one channel, so that the slight modification is almost indistinguishable to naked eyes, and the effect of destroying the steganographic malicious code in the image can be achieved.
And 240, copying the updated pixel values corresponding to the pixel points into the blank image to obtain the target image.
Step 250, inputting the target image into a depth self-encoder, and processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image.
And 260, storing the coding vector corresponding to the target image in a server.
And step 270, responding to an image reading request triggered by a user, acquiring a coded vector corresponding to the target image from the server, and processing the coded vector through a decoding layer of a depth self-encoder to obtain the target image.
In this embodiment, the depth self-encoder includes an encoding layer and a decoding layer. After storing the encoding vector corresponding to the target image in the server, if the user needs to read and display the target image from the server, an image reading request may be triggered. After the image reading request is received, the coding vector corresponding to the target image can be obtained from the server, and then the decoding layer of the depth self-encoder is used for restoring the coding vector to obtain the safe target image.
According to the technical scheme provided by the embodiment of the invention, the original image and the target size input by a user are acquired, a blank image consistent with the original image size is established according to the target size, target metadata in the original image is copied into the blank image, RGB values of each pixel point in the original image are acquired, target bit values are randomly extracted from the RGB values of each pixel point according to a preset bit number, the target bit values corresponding to each pixel point are inverted, updated pixel values corresponding to each pixel point are copied into the blank image to obtain the target image, the target image is input into a depth self-encoder, the target image is processed through a coding layer of the depth self-encoder to obtain a coding vector, the coding vector is stored in a server, the coding vector is processed through a decoding layer of the depth self-encoder in response to an image reading request triggered by the user, and the technical means of the target image is obtained, so that the safety processing efficiency of the image can be improved, and the safety of image data entering the server is ensured.
Fig. 3 is a flowchart of another image security processing method according to an embodiment of the present invention, as shown in fig. 3, where the method includes:
step 310, constructing a depth self-encoder, and inputting the image training samples to a current processing layer in the depth self-encoder.
Step 320, training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer.
In one implementation manner of this embodiment, training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer includes: determining a mean square error between the image training sample and an output result of the current processing layer; and taking the mean square error as a loss function, and training according to the loss function to obtain the weight corresponding to the current processing layer.
In this embodiment, the training goal of the depth self-encoder is to make the encoded and decoded image as identical as possible to the input image, so the training process uses the mean square error between the input image training samples and the output image as a loss function.
And 330, taking the output result of the current processing layer as input data of a subsequent processing layer, and training to obtain the weight corresponding to the subsequent processing layer according to the output result of the subsequent processing layer.
In this embodiment, specifically, a depth self-encoder with a depth of 1 may be constructed first, and the weights thereof are trained, after the training is finished, the output of the 1 st layer is used as input, a new processing layer is constructed for training, so as to obtain the 2 nd layer weights, and then the weights of each layer are sequentially trained in the same manner.
Step 340, determining whether the number of processing layers in the depth self-encoder meets a preset threshold, if yes, executing steps 350-360, if not, executing step 370, and returning to execute the operation of inputting the image training sample to the current processing layer in the depth self-encoder in step 310 until the number of processing layers in the depth self-encoder meets the preset threshold.
In this embodiment, the structural design of the depth self-encoder depends on the requirements of the system on the security level of the image, and the more the number of processing layers in the depth self-encoder is, the shorter the output data of the encoded image is, the larger the binary data of the image is changed, and the more difficult malicious codes possibly embedded in the image are kept. Meanwhile, the depth self-encoder with more processing layers has smaller vector length formed after image encoding and lower memory space cost.
In this step, it may be determined that the number of processing layers in the depth self-encoder satisfies a preset threshold, if yes, the depth self-encoder is applied to the image security processing service, and if not, training is continued on the processing layers in the depth self-encoder until the number of processing layers satisfies the preset threshold.
Step 350, obtaining an original image input by a user, establishing a blank image according to the original image, copying target metadata in the original image into the blank image, performing random bit operation on pixel values of each pixel point in the original image, and copying updated pixel values corresponding to each pixel point into the blank image to obtain the target image.
And 360, inputting the target image into a depth self-encoder, processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image, and storing the encoding vector corresponding to the target image in a server.
And 370, re-using the subsequent processing layer as the current processing layer.
According to the technical scheme provided by the embodiment of the invention, the image training sample is input to the current processing layer in the depth self-encoder by constructing the depth self-encoder, the weight corresponding to the current processing layer is obtained by training according to the output result of the current processing layer, the output result of the current processing layer is used as the input data of the subsequent processing layer, the weight corresponding to the subsequent processing layer is obtained by training according to the output result of the subsequent processing layer, if the number of the processing layers in the depth self-encoder meets the preset threshold value, the original image input by a user is obtained, the blank image is established according to the original image, the target metadata in the original image is copied in the blank image, the random bit operation is carried out on the pixel value of each pixel point in the original image, the updated pixel value corresponding to each pixel point is copied in the blank image, the target image is obtained, the target image is input into the depth self-encoder, the coding vector corresponding to the target image is obtained by processing the coding layer of the depth self-encoder, the coding vector corresponding to the target image is stored in the server, the safety processing efficiency of the image can be improved, and the safety of the image data entering the server is ensured.
Fig. 4 is a schematic structural diagram of an image security processing apparatus according to an embodiment of the present invention, where the apparatus is applied to an electronic device, as shown in fig. 4, and the apparatus includes: an image acquisition module 410, an image processing module 420, an image encoding module 430, and an image storage module 440.
An image acquisition module 410, configured to acquire an original image input by a user, and establish a blank image according to the original image;
the image processing module 420 is configured to copy the target metadata in the original image into a blank image, perform random bit operation on a pixel value of each pixel point in the original image, and copy an updated pixel value corresponding to each pixel point into the blank image to obtain a target image;
the image encoding module 430 is configured to input the target image into a depth self-encoder, and process the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image;
the image storage module 440 is configured to store the encoding vector corresponding to the target image in a server.
According to the technical scheme provided by the embodiment of the invention, the blank image is established according to the original image by acquiring the original image input by a user, the target metadata in the original image is copied into the blank image, the pixel value of each pixel point in the original image is subjected to random bit operation, the updated pixel value corresponding to each pixel point is copied into the blank image to obtain the target image, the target image is input into the depth self-encoder, the target image is processed through the coding layer of the depth self-encoder to obtain the coding vector corresponding to the target image, and the coding vector corresponding to the target image is stored in the server.
On the basis of the above embodiment, the apparatus further includes:
the self-encoder construction module is used for constructing a depth self-encoder and inputting image training samples to a current processing layer in the depth self-encoder;
the current layer training module is used for training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer;
the subsequent layer training module is used for taking the output result of the current processing layer as input data of the subsequent processing layer and training to obtain the weight corresponding to the subsequent processing layer according to the output result of the subsequent processing layer;
and the iteration training module is used for re-using the subsequent processing layers as the current processing layers and returning to execute the operation of inputting the image training samples to the current processing layers in the depth self-encoder until the number of the processing layers in the depth self-encoder meets a preset threshold value.
The current layer training module comprises:
the mean square error determining unit is used for determining the mean square error between the image training sample and the output result of the current processing layer;
and the loss function determining unit is used for taking the mean square error as a loss function and obtaining the weight corresponding to the current processing layer according to the loss function training.
The image acquisition module 410 includes:
the blank image establishing unit is used for acquiring a target size corresponding to the original image and establishing a blank image consistent with the original image size according to the target size.
The image processing module 420 includes:
the pixel value acquisition unit is used for acquiring the RGB value of each pixel point in the original image, and randomly extracting a target bit value from the RGB value of each pixel point according to a preset bit number;
and the pixel value inverting unit is used for inverting the target bit number value corresponding to each pixel point.
The image storage module 440 includes:
a reading request response unit, configured to obtain, from the server, a coding vector corresponding to the target image in response to an image reading request triggered by a user;
and the decoding unit is used for processing the coded vector through a decoding layer of the depth self-encoder to obtain a target image.
The device can execute the method provided by all the embodiments of the invention, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in the embodiments of the present invention can be found in the methods provided in all the foregoing embodiments of the present invention.
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as an image security processing method.
In some embodiments, the image security processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the image security processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the image security processing method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of image security processing, the method comprising:
acquiring an original image input by a user, and establishing a blank image according to the original image;
copying target metadata in the original image into a blank image, performing random bit operation on pixel values of each pixel point in the original image, and copying updated pixel values corresponding to each pixel point into the blank image to obtain a target image;
inputting the target image into a depth self-encoder, and processing the target image through an encoding layer of the depth self-encoder to obtain an encoding vector corresponding to the target image;
and storing the coding vector corresponding to the target image in a server.
2. The method of claim 1, wherein performing a random bit operation on the pixel value of each pixel in the original image comprises:
acquiring an RGB value of each pixel point in the original image, and randomly extracting a target bit value from the RGB value of each pixel point according to a preset bit number;
and inverting the target bit number value corresponding to each pixel point.
3. The method of claim 1, wherein prior to acquiring an original image input by a user and creating a blank image from the original image, further comprising:
constructing a depth self-encoder, and inputting image training samples to a current processing layer in the depth self-encoder;
training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer;
taking the output result of the current processing layer as input data of a subsequent processing layer, and training to obtain the weight corresponding to the subsequent processing layer according to the output result of the subsequent processing layer;
and re-using the subsequent processing layer as the current processing layer, and returning to execute the operation of inputting the image training sample to the current processing layer in the depth self-encoder until the number of the processing layers in the depth self-encoder meets a preset threshold value.
4. A method according to claim 3, wherein training to obtain the weight corresponding to the current processing layer according to the output result of the current processing layer comprises:
determining a mean square error between the image training sample and an output result of the current processing layer;
and taking the mean square error as a loss function, and training according to the loss function to obtain the weight corresponding to the current processing layer.
5. The method of claim 1, wherein creating a blank image from the original image comprises:
and acquiring a target size corresponding to the original image, and establishing a blank image with the same size as the original image according to the target size.
6. The method according to claim 1, further comprising, after storing the encoded vector corresponding to the target image in a server:
responding to an image reading request triggered by a user, and acquiring a coding vector corresponding to the target image from the server;
and processing the coded vector through a decoding layer of the depth self-encoder to obtain a target image.
7. An image security processing apparatus, characterized in that the apparatus comprises:
the image acquisition module is used for acquiring an original image input by a user and establishing a blank image according to the original image;
the image processing module is used for copying the target metadata in the original image into a blank image, carrying out random bit operation on the pixel value of each pixel point in the original image, and copying the updated pixel value corresponding to each pixel point into the blank image to obtain a target image;
the image coding module is used for inputting the target image into a depth self-encoder, and processing the target image through a coding layer of the depth self-encoder to obtain a coding vector corresponding to the target image;
and the image storage module is used for storing the coding vector corresponding to the target image in a server.
8. The apparatus of claim 7, wherein the image processing module comprises:
the pixel value acquisition unit is used for acquiring the RGB value of each pixel point in the original image, and randomly extracting a target bit value from the RGB value of each pixel point according to a preset bit number;
and the pixel value inverting unit is used for inverting the target bit number value corresponding to each pixel point.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image security processing method of any one of claims 1-6.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the image security processing method of any one of claims 1-6 when executed.
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