In recent years, linguistic steganography based on text auto-generation technology has been greatly developed, which is considered to be a very promising but also a very challenging research topic. Previous works mainly focus on optimizing the language model and conditional probability coding methods, aiming at generating steganographic sentences with better quality. In this paper, we first report some of our latest experimental findings, which seem to indicate that the quality of the generated steganographic text cannot fully guarantee its steganographic security, and even has a prominent perceptual-imperceptibility and statistical-imperceptibility conflict effect (Psic Effect). To further improve the imperceptibility and security of generated steganographic texts, in this paper, we propose a new linguistic steganography based on Variational Auto-Encoder (VAE), which can be called VAE-Stega. We use the encoder in VAE-Stega to learn the overall statistical distribution characteristics of a large number of normal texts, and then use the decoder in VAE-Stega to generate steganographic sentences which conform to both of the statistical language model as well as the overall statistical distribution of normal sentences, so as to guarantee both the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic texts at the same time. We design several experiments to test the proposed method. Experimental results show that the proposed model can greatly improve the imperceptibility of the generated steganographic sentences and thus achieves the state of the art performance.

VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder

Automatic cost learning for steganography based on deep neural networks is receiving increasing attention. Steganographic methods under such a framework have been shown to achieve better security performance than methods adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality, including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.

An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning

For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of rich models (RMs), followed by classification using an ensemble classifier (EC). In 2015 the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by deep learning approaching the performances of the two-step approach (EC + RM). During 2015–2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, selection-channel-aware steganalysis, and quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of current methods, by presenting different neural networks from the period 2015–2018, evaluated with a methodology specific to the discipline of steganalysis. We do not intend to repeat the basic concepts of machine learning or deep learning. So we will present the structure of a deep neural network in a generic way and present the networks proposed in the existing literature for different scenarios of steganalysis, and finally, we will discuss steganography by deep learning.

Deep learning in steganography and steganalysis

The amount of data exchanged via the Internet is increasing nowadays. Hence, data security is termed as a serious issue while communication of data is processed over the Internet. Everyone needs th...

Analysis of various data security techniques of steganography: A survey

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 dependencies 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.

Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model

Robust adaptive steganography based on generalized dither modulation and expanded embedding domain

Steganography is an information hiding technique for covert communication. So far Syndrome-Trellis Codes (STC), a convolutional codes-based method, is the only near-optimal coding method, i.e., it can approach the rate-distortion bound of content-adaptive steganography in practice. However, as a secure communication application, steganography needs the diversity of coding methods. This paper proposes another and a better near-optimal steganographic coding method based on polar codes, using Successive Cancellation List (SCL) decoding algorithm to minimize additive distortion in steganography. Considering a steganographic channel as a binary symmetric channel, the proposed Steganographic Polar Codes (SPC) chooses parity-check matrix by setting embedding payload as the initial value of Arikan’s heuristic and computes decoding channel metric from the optimal modification probability of minimal distortion model. To overcome the inherent defect of polar codes only suiting for code length of a power of 2, we introduce three strategies to generalize SPC for arbitrary length. Experimental results validate the versatility of SPC to minimize arbitrary distortion. When compared with STC, the overall coding performance of SPC is more superior with low embedding complexity. This work verifies the availability of polar codes for the practical construction of steganographic codes and provides a methodology for designing better steganographic codes based on any advance of polar coding/decoding.

Designing Near-Optimal Steganographic Codes in Practice Based on Polar Codes

Recent studies have shown that the non-additive distortion model of Decomposing Joint Distortion ($DeJoin$) can work well for spatial image steganography by defining joint distortion with the principle of Synchronizing Modification Directions (SMD). However, no principles have yet produced to instruct the definition of joint distortion for JPEG steganography. Experimental results indicate that SMD can not be directly used for JPEG images, which means that simply pursuing modification directions clustered does not help improve the steganographic security. In this paper, we inspect the embedding change from the spatial domain and propose a principle of Block Boundary Continuity (BBC) for defining JPEG joint distortion, which aims to restrain blocking artifacts caused by inter-block adjacent modifications and thus effectively preserve the spatial continuity at block boundaries. According to BBC, whether inter-block adjacent modifications should be synchronized or desynchronized is related to the DCT mode and the adjacent direction of inter-block coefficients (horizontal or vertical). When built into $DeJoin$, experiments demonstrate that BBC does help improve state-of-the-art additive distortion schemes in terms of relatively large embedding payloads against modern JPEG steganalyzers.

Defining Joint Distortion for JPEG Steganography

Despite the strong presence of color images for communication, scholars have mainly devoted their attention to research on steganography for grayscale images. In contrast to grayscale images, color images have three interrelated color channels, and the relationships among the three channels have a strong impact on the steganography security. In this paper, we present a steganographic scheme for spatial color images by exploiting the correlations and differences between the color channels. We find that the G channel has a stronger correlation with R and B than the one between R and B, and thus, synchronizing the modification directions of the R and B channels with those from the G channel will have better resistance to detection. In addition, the payload capacity and the distribution of complex regions between channels are different. Based on these findings, we design a new strategy for defining non-additive costs for color image steganography, called G-channel-related Inter-channel Non-Additive (GINA) strategy. The GINA strategy can make the modification directions of the R and B channels consistent with those of the G channel and can adaptively distribute the embedding capacity between the three channels. Specifically, this strategy will not violate the Complexity Prior rule. The experimental results show that the proposed GINA strategy can significantly improve the performance in terms of resisting color image steganalysis compared with previous methods.

Non-Additive Cost Functions for Color Image Steganography Based on Inter-Channel Correlations and Differences

Recently, Li et al. proposed an effective non-additive distortion model for JPEG steganography by preserving Block Boundary Continuity (BBC) in the spatial domain. However, the method based on BBC only explored how the modifications of DCT coefficient pairs at the same mode in adjacent blocks will impact on BBC. In this paper, we propose a method to enhance the BBC, called BBC++, by considering the mutual impact on the BBC from all DCT coefficients in adjacent blocks. To do that, we design updating strategies for both covers and costs in multi-round embedding processions. During the embedding, the cover is updated to repair the BBC after embedding and the corresponding costs are updated to keep the BBC from being destroyed in the next embedding. Experimental results show that the BBC++ can better maintain BBC and outperform previous non-additive distortion steganography when resisting modern JPEG steganalyzers.

Weixiang Li

Papers

Robust adaptive steganography based on generalized dither modulation and expanded embedding domain

Designing Near-Optimal Steganographic Codes in Practice Based on Polar Codes

Defining Joint Distortion for JPEG Steganography

Non-Additive Cost Functions for Color Image Steganography Based on Inter-Channel Correlations and Differences

BBC++: Enhanced Block Boundary Continuity on Defining Non-Additive Distortion for JPEG Steganography