Research On Evolutionary Methods Of Distortion Function For Image Adaptive Steganography

The development of information technology and the advancement of the Inter-net have made network interaction increasingly frequent and convenient.The network interaction is inundated with information about military,political,financial and com-mercial which are sensitive for countries,enterprises and individuals.In order to ensure the security of data,encryption technology is commonly used to encode plaintext se-cret messages into meaningless ciphertexts,however,the behavior of encryption brings potential hidden danger,which revealing the existence of hidden information,and thus attracting attacks.The digital information hiding technology which has the attibution of "disguise" can ensure the security of the transmitted data while making the behavior of the information transmission difficult to be perceived.As an important part of in-formation hiding technology,image adaptive steganography plays an important role in the protection of information security.By defining reasonable distortion of the cover image elements,the appropriate cover elements are selected for secret message embed-ding.However,with the rapid development of high-dimensional feature based adaptive steganalysis and deep learning based steganalysis techniques,image adaptive steganog-raphy faces new challenges.Traditional single-form distortion functions can no longer meet the security requirements of steganography.It is of great significance and value to study the evolutionary methods of distortion function for image adaptive steganog-raphy.To improve the security of image adaptive steganography,three key problems need to be solved.First is to break the monotony of the steganographic algorithm.Secondly,to interfere with the targeted analysis of high-dimensional feature based adaptive ste-ganalysis.Thirdly,to counterwork the efficient learning ability of deep learning based steganalysis.Focusing on these three key issues,this dissertation studies the evolution-ary methods of distortion function for image adaptive steganography.The main work and innovations of this dissertation are as follows:1.Investigated the Evolving Model for Distortion Function in Spatial Image Adaptive SteganographyThe research on spatial image adaptive steganography focuses on the reasonable definition of distortion function.In order to resist the targeted analysis of high-dimensional feature based adaptive steganalysis,this dissertation proposes a evolving model for dis-tortion function in spatial image adaptive steganography.By mining the differences between the existing fundamental distortion function algorithms,the"controversial pix-els" in the spatial image are defined.Steganalysis features are often insensitive to this part of the controversial pixel,so by promoting the modification probabilities,more se-cret messages are assigned to the controversial pixels,and a new distortion function is derived therefrom.The number of basic distortion functions used in this model can be varied,and the new type of derived distortion function is not fixed.The experimental results show that the distortion function algorithm derived from this model has better security than the basic distortion function algorithm.2.Investigated the Variation Mechanism for Distortion Function in JPEG Image Adaptive SteganographyAs the most popular image format,JPEG images adaptive steganography is of great significance for information security.The cover elements of JPEG steganogra-phy are quantized DCT coefficients.Similar to spatial image steganography,in JPEG adaptive steganography,the controversial DCT coefficients can be defined by mining the controversy of the existing basic distortion function algorithm.However,due to the particularity of the JPEG images,it is necessary to fully consider the cover characteris-tics when defining the distortion.In this dissertation,the block energy,which measures the image texture complexity in JPEG image,is utilized to optimizing the adjustment weight of the controversial DCT coefficient.The new distortion function algorithm is generated from several basic distortion functions.The experimental results show that the security of new distortion function algorithm has a significant improvement com-pared with the basic distortion function algorithm.3.Proposed the Multi-block Combination based Distortion Function Diversi-fication AlgorithmIn order to interfere with the targeted feature extraction of high-dimensional fea-ture steganalysis,this dissertation proposes a multi-block combination based distortion function diversification algorithm from image level and pixel level respectively.Firstly,based on the ensemble steganography scheme,the cover image is randomly partitioned and combined with a variety of different types of distortion functions,so that the defi-nition of distortion function has randomness and dynamics.On this basis,based on the folding embedding scheme,the strong randomness is further introduced from the pixel level,and the embedding efficiency of the steganographic coding is greatly improved while developing the diversification of the distortion function.Experiments show that the proposed method can effectively interfere with the estimation of modification prob-abilities for cover element of high-dimensional feature based steganalysis and achieve security improvement.4.Proposed the Adversarial Evolution Method for Distortion FunctionThe development of deep learning based steganalysis puts higher demands on the design of adaptive steganographic distortion functions.In order to resist the analysis targeted on the modification mod of steganography,this dissertation proposes an ad-versarial evolution method for distortion function.Combine deep learning adversarial examples technique with steganography to attack the vulnerability of deep learning net-works.In this scheme,the gradient information of adversarial examples generated by the steganalysis network is used to adjust the basic distortion in different modification directions,and a new distortion function with adversarial characteristics is evolved,which can cause a failure in deep learning based steganalysis.Experiments show that the proposed method can effectively resist the detection of deep learning based ste-ganalysis,and also obtain a certain degree of security improvement when resisting high-dimensional feature based steganalysis.