Research On Steganography Of Multiple Covers And New Covers

In recent years,the popularization of the Internet has promoted the efficient transmission of information.However,sensitive information related to military,political,commercial,and other important fields is also exposed on the network,so the security of the sensitive information transmitted on the Internet should be protected.To this end,classical cryptography encodes the information to a series of meaningless and random codewords to hide the information content,thereby protecting the content security of sensitive information.However,the behavior of "secret communication" is divulged by the way of sending such meaningless codewords,which may arouse targeted monitoring and attack.In order to hide the "secret communication" behavior,steganography hides the encrypted messages in the common multimedia,and then transmits them through public channels such as social platforms,in such a way the "secret communication" is covered up by the normal social behavior,thereby the behavior security of communication is protected.In practice,steganography usually faces the risk of steganalysis,including single image steganalysis and steganographer detection based on users.A secure steganography algorithm not only requires the concealment of the modification but also expects the steganographer’s behavior conforms to the social scene.Batch steganography conforms to the behavior of social users in terms of the number of images,since the messages are hiding in multiple images.But existing research on batch steganography mainly focuses on the problem of payload distribution but ignores other behavior characteristics of the steganographer,thereby the steganographer is still liable to expose herself under behavior analysis.Additionally,deep learning-based steganographer detection brings new challenges to batch steganography.Therefore,this dissertation first studies how to improve the security of multi-cover-based batch steganography,and then makes use of the new covers brought by deep learning to design steganography algorithms.The main work and innovations of this dissertation are as follows:1.Behavioral Secure Cover Selection Method for Batch Steganography To improve the behavior security of the steganographer,two discriminators are designed,namely discriminators based on side-channel steganalysis and complementary attack.The side-channel analysis is used to distinguish random image sequences from content-related image sequences.Because social users usually send images with similar content continuously,selecting the image sequence with related content as the cover by using side-channel analysis helps the steganographer to behave consistently with the social user.It is further found that content-related images could be used as side information to improve the accuracy of steganalysis,which forms a complementary attack with side-channel analysis.Therefore,the steganographer should not send too many content-related images.Experimental results demonstrate that the number of content-relative images should be in a secure interval,neither totally random covers nor totally relative covers are secure.2.Batch Adversarial Steganography Against Deep-learning Based Steganographer DetectionTo improve the security of batch steganography under deep learning-based steganographer detection,this dissertation proposes batch adversarial steganography against unsupervised steganographer detection.The proposed approach is based on the idea of the adversarial example in computer vision.a novel cost function is designed regarding the unsupervised classification approach of steganographer detection,then the gradient obtained by its back-propagation is used to adjust the traditional steganography distortion in such a way that the modification direction of steganography could minimize the distance between the steganographer and the normal user,thereby reducing the risk of steganographer being found.Finally,the strategy is implemented to distribute the payload between images.Experimental results demonstrate that the proposed method increases the detection errors of the attacked steganographer detection and single image steganalysis,thereby improving the security of the steganographic algorithm.3.Image Steganography Based on Style TransferCurrently,it is a common phenomenon to share stylized images on the Internet,and hiding information with stylized images can achieve covert communication.This dissertation proposes a steganography method based on image style transfer.It integrates the image style transfer process with the message embedding process to generate stages that are difficult to distinguish from the stylized image,thereby improving the security of steganography.To implement the message embedding and style transfer at the same time,the proposed method concat the secret message and the shallow feature map of a style transfer network.To correctly extract the embedded messages,a message extraction network is added at the end of the message embedding network.Moreover,we adopt the idea of adversarial training and use a steganalysis network as the discriminator to improve the security of generated stegos.Experimental results demonstrate that this method can achieve high-capacity steganography and the generated stegos are difficult to distinguish from stylized images.4.Steganography Based on Deep Neural NetworksWith the popularity of open-source platforms of deep learning models,sharing deep neural networks in the network has become a common social behavior.Therefore,the deep neural network becomes an ideal cover for steganography.This dissertation proposes two steganography methods based on deep neural networks:the covert task steganography method and the Black-box robust steganography method.In covert task steganography,the network training process is modified to force the network to learn a hidden task in addition to the primary task,and hides the hidden task result as a secret message in the output of the primary task.In black-box robust steganography.The output of the network is related to the secret message by a key sequence.By adding a regular term to the training loss,the output of the primary task is forced to have a specific deviation,which is related to the secret message and key.The receiver can directly extract the secret message from the network output by using the key.The experimental results verified the efficiency of the two proposed methods,the hidden task results and the hidden message could be extracted accurately respectively,without affecting the performance of the original task.