When Provably Secure Steganography Meets Generative Models

Steganography is the art and science of hiding secret messages in public communication so that the presence of the secret messages cannot be detected. There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based. The former requires a perfect sampler which yields data following the same distribution, and the latter needs explicit distributions of generative objects. However, these two conditions are too strict even unrealistic in the traditional data environment, because it is hard to model the explicit distribution of natural image. With the development of deep learning, generative models bring new vitality to provably secure steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media. Motivated by this, this paper proposes two types of provably secure stegosystems with generative models. Specifically, we first design block-box sampling based provably secure stegosystem for broad generative models without explicit distribution, such as GAN, VAE, and flow-based generative models, where the generative network can serve as the perfect sampler. For compression based stegosystem, we leverage the generative models with explicit distribution such as autoregressive models instead, where the adaptive arithmetic coding plays the role of the perfect compressor, decompressing the encrypted message bits into generative media, and the receiver can compress the generative media into the encrypted message bits. To show the effectiveness of our method, we take DFC-VAE, Glow, WaveNet as instances of generative models and demonstrate the perfectly secure performance of these stegosystems with the state-of-the-art steganalysis methods

Identification of Image Operations Based on Steganalytic Features

Image forensics have attracted wide attention during the past decade. Though many forensic methods have been proposed to identify image forgeries, most of them are targeted ones, since their proposed features are highly dependent on the image operation under investigation. The performance of the well-designed features for detecting the targeted operation usually degrades significantly for other operations. On the other hand, a wise attacker can perform anti-forensics to fool the existing forensic methods, making countering anti-forensics become an urgent need. In this paper, we try to find a universal feature to detect various image processing and anti-forensic operations. Based on our extensive experiments and analysis, we find that any image processing/anti-forensic operations would inevitably modify many image pixels. This would change some inherent statistics within original images, which is similar to the case of steganography. Therefore, we model image processing/anti-forensic operations as steganography problems, and propose a detection strategy by applying steganalytic features. With some advanced steganalytic features, we are able to detect various image operations and further identify their types. In our experiments, we have tested several steganalytic features on 11 different kinds of typical image processing operations and 4 kinds of anti-forensic operations. The experimental results have shown that the proposed strategy significantly outperforms the existing forensic methods in both effectiveness and universality

How to augment a small learning set for improving the performances of a CNN-based steganalyzer?

Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that "the bigger a database is, the better the results are". However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency. To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance. What kind of images can be added to the initial learning set? What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc? This article continues the work carried out in a previous paper, and explores the ways to improve the performances of CNN. It aims at studying the effects of "base augmentation" on the performance of steganalysis using a CNN. We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis.Comment: EI'2018, in Proceedings of Media Watermarking, Security, and Forensics, Part of IS&T International Symposium on Electronic Imaging, San Francisco, California, USA, 28 Jan. -2 Feb. 2018, 7 page

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Adaptive spatial image steganography and steganalysis using perceptual modelling and machine learning

Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research.Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research

Game-theoretic Analysis to Content-adaptive Reversible Watermarking

While many games were designed for steganography and robust watermarking, few focused on reversible watermarking. We present a two-encoder game related to the rate-distortion optimization of content-adaptive reversible watermarking. In the game, Alice first hides a payload into a cover. Then, Bob hides another payload into the modified cover. The embedding strategy of Alice affects the embedding capacity of Bob. The embedding strategy of Bob may produce data-extraction errors to Alice. Both want to embed as many pure secret bits as possible, subjected to an upper-bounded distortion. We investigate non-cooperative game and cooperative game between Alice and Bob. When they cooperate with each other, one may consider them as a whole, i.e., an encoder uses a cover for data embedding with two times. When they do not cooperate with each other, the game corresponds to a separable system, i.e., both want to independently hide a payload within the cover, but recovering the cover may need cooperation. We find equilibrium strategies for both players under constraints.Comment: 12 pages, 2 figure

Image Steganography using Gaussian Markov Random Field Model

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependences among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers

Pooled Steganalysis in JPEG: how to deal with the spreading strategy?

In image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonably assess that the steganalyst does not know the strategy used to spread the payload across images. To the best of our knowledge, in this case, the most appropriate solution for pooled steganalysis is to use a Single-Image Detector (SID) to estimate/quantify if an image is cover or stego, and to average the scores obtained on the set of images. In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known spreading strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach. Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy. Another interesting observation is that DeLS spreading strategy behaves really better than all the other spreading strategies. Those observations results in the experimentation with six different spreading strategies made on Jpeg images with J-UNIWARD, a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies.Comment: Ahmad Zakaria, Marc Chaumont, Gerard Subsol, " Pooled Steganalysis in JPEG: how to deal with the spreading strategy? ", WIFS'2019, IEEE International Workshop on Information Forensics and Security, December 9-12, 2019, Delft, The Netherlands, 6 pages, Acceptance rate = 30

CNN-based Steganalysis and Parametric Adversarial Embedding: a Game-Theoretic Framework

CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can be restored by retraining the CNN with adversarial stego images. A problem with this model is that, arguably, at training time the steganalizer is not aware of the exact parameters used by the steganograher for adversarial embedding and, vice versa, the steganographer does not know how the images that will be used to train the steganalyzer are generated. In order to exit this apparent deadlock, we introduce a game theoretic framework wherein the problem of setting the parameters of the steganalyzer and the steganographer is solved in a strategic way. More specifically, a non-zero sum game is first formulated to model the problem, and then instantiated by considering a specific adversarial embedding scheme setting its operating parameters in a game-theoretic fashion. Our analysis shows that the equilibrium solution of the non zero-sum game can be conveniently found by solving an associated zero-sum game, thus reducing greatly the complexity of the problem. Then we run several experiments to derive the optimum strategies for the steganographer and the staganalyst in a game-theoretic sense, and to evaluate the performance of the game at the equilibrium, characterizing the loss with respect to the conventional non-adversarial case. Eventually, by leveraging on the analysis of the equilibrium point of the game, we introduce a new strategy to improve the reliability of the steganalysis, which shows the benefits of addressing the security issue in a game-theoretic perspective.Comment: Adversarial embedding, deep learning, steganography, steganalysis, game theor