Syndrome-trellis Codes Research Articles

A New Distortion Function Design for JPEG Steganography Using the Generalized Uniform Embedding Strategy

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time.

A Spatial Image Steganography Method Based on Nonnegative Matrix Factorization

Research on adaptive steganography mainly focuses on how to design a reasonable cost function and how to utilize that cost function to achieve embedding in a stego image with the minimal distortion based on syndrome-trellis codes. Because previous adaptive steganographic methods use convolution with filters to obtain the residuals, these methods do not make good use of the textures of the image itself in the design of the cost function. In this letter, we define a new cost function that uses nonnegative matrix factorization to predict the image pixels and utilizes the mutual dependencies among the pixels to calculate the costs. We present a novel cost function in which the residuals are not calculated via convolution with constant filters. Experimental results show that our method outperforms the state-of-the-art MiPOD, spatial universal wavelet relative distortion, wavelet obtained weights, and HUGO-BD methods in resisting steganalysis based on the spatial rich model and is slightly superior to the high-pass, low-pass, low-pass method.

Steganography using Gabor filter and anisotropic diffusion

In order to improve the statistical security and the ability of resistance to steganalysis of adaptive steganography, an adaptive steganography algorithm based on Gabor filters and anisotropic diffusion is proposed, which can be utilized in an arbitrary domain. Compared with other adaptive embedding schemes, this method can define image distortion more accurately. Firstly, Gabor filters with multi-orientation and multi-scale is employed to determine the noisy regions or textures of cover image. Then the P-M anisotropic diffusion model is utilized to make image elements with low distortion cost relatively cluster in complex areas of image, and better retain the image edge information. At last, the syndrome-trellis codes is used to realize the actual embedding method to minimize the expected distortion for a given payload. The experimental results illustrate that the proposed method can significantly improve the accuracy of embedding in noisy regions and textures, and gains a better performance on resisting the current state-of-the-art steganalysis over prior algorithms.

Decomposing Joint Distortion for Adaptive Steganography

Recent advances on adaptive steganography imply that the security of steganography can be improved by exploiting the mutual impact of modifications between adjacent cover elements, such as pixels of images, which is called a nonadditive distortion model. In this paper, we propose a framework for nonadditive distortion steganography by defining joint distortion on pixel blocks. To reduce the complexity for minimizing joint distortion, we design a coding method to decompose the joint distortion (abbreviated to DeJoin) into distortion on individual pixels; thus, the message can be efficiently embedded with syndrome-trellis codes. We prove that DeJoin can approach the lower bound of joint distortion. As an example, we define joint distortion according to the principle of synchronizing modification direction and then design steganographic algorithms with DeJoin. The experimental results show that the proposed method outperforms previous nonadditive distortion steganography when resisting the state-of-the-art steganalysis.

Reliable steganalysis of HUGO steganography based on partially known plaintext

The image adaptive steganography represented by HUGO (Highly Undetectable steGO) has high anti-detection capabilities. The primary challenge for steganalyzers is how to reliably detect such steganography and extract the embedded message from stego. Although existing steganalysis algorithms based on parameter recognition of STCs (Syndrome-Trellis Codes) can reliably detect adaptive steganography when the embedded message is in plaintext, the steganalysis method is ineffective when the embedded message is in ciphertext. Therefore, a steganalysis algorithm based on partially known plaintext is proposed in this paper. The steganalysis algorithm targets situations wherein in order to facilitate the receiver’s extraction and storage of the embedded message, the file format name and message length may be transported without encryption when HUGO steganography is used to transport the encrypted file. First, the structural characteristics of the parity-check matrix were utilized to simplify the STCs decoding equation. Second, we calculated the submatrix by solving nonhomogeneous linear equations instead of exhausting submatrix, thus, parameter recognition efficiency was significantly improved. Finally, we verified the correctness of the submatrix and extracted the embedded message from stego images. The experimental results show that parameter recognition of STCs can be achieved using an ordinary PC within a short time. Therefore, the embedded message can be extracted when the partly embedded message is known.

Adaptive Spatial Steganography Based on the Correlation of Wavelet Coefficients for Digital Images in Spatial Domain

How to define the pixel’s embedding distortion is a key problem of image adaptive steganography. This paper proposes an adaptive spatial steganography in spatial domain based on the correlation of wavelet coefficients for digital images according to the principle of minimizing a suitable defined distortion. Firstly, one-dimensional high-pass and low-pass filters are used to construct directional filters. Secondly, do direction filtering to images through the direction of horizontal, vertical and diagonal, and design the distortion cost function according to the correlation of wavelet coefficients. Finally, the secret message is embedded by STC (Syndrome Trellis Code) according to the pixel’s embedding distortion. The experiment results illustrate that the proposed method not only make the embedding region focus on the texture regions whose content is complex, but also can resist the common steganalysis detection effectively.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding

ObjectivesThe present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images. MethodsThe proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs’ confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications. ResultsThe experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques. ConclusionThe proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data.

ENGLISH

Steganography is the art of hidden inscription, used prominently to cover text, image, video and audio. The persistence of steganography is to hide the presence of a message from a third party. The intention of this paper is to achieve statistical security without degrading the stego image quality and to improve undetectability. This paper conceals the secret image behind the original image to obtain the stego image. The extraction of inversion pattern, rotation pattern and invariant local binary pattern is preceded to calculate the distortion of cover image. The obtained image is scrambled in order to distribute the pixels uniformly. To reduce the embedding impact of image and to improve the statistical security, Syndrome Trellis Code (STC) is employed which mainly uses the convolutional code with Viterbi algorithm. The steganography application is realized in STC technique by covering the binary image over the original image. The resultant stego image is obtained after proceeding with the STC technique. The proposed technique is simulated in MATLAB and implemented in Spartan 3 FPGA, interfaced with VGA controller to ensure in real time applications.

Covert Communication by Compressed Videos Exploiting the Uncertainty of Motion Estimation

This letter deals with information hiding in compressed video; its goal is to improve the security of motion vector-based steganography with respect to the current best steganalytic method. Our research suggests that the perfect security (undetectability) can be approached if the uncertain information, which exists in the process of motion estimation, is fully exploited and establishes a connection between single motion vector's embedding distortion and its associated uncertainty. Based on this, a novel video steganography is proposed. Under a given payload, the syndrome-trellis code is used to minimize the overall embedding impact. Experimental results show that the proposed method outperforms other existing motion vector-based approaches for a higher level of security and a smaller impact on coding performance.

Secure Binary Image Steganography Based on Minimizing the Distortion on the Texture

Most state-of-the-art binary image steganographic techniques only consider the flipping distortion according to the human visual system, which will be not secure when they are attacked by steganalyzers. In this paper, a binary image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the binary image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple binary images and the constructed image data set, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled image into superpixels. Thereafter, the syndrome-trellis code is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the image quality or the embedding capacity.

Damage‐resistance matrix embedding framework: the contradiction between robustness and embedding efficiency

AbstractMatrix embedding schemes based on linear codes have been widely used in the field of steganography, which is an important branch of covert communication. Nevertheless, they appear weak for some conditions of high reliability demand or “active attack” because of the poor robustness. In this paper, with robustness analysis of matrix embedding based on Tanner graph, a general framework of damage‐resistance matrix embedding (DR‐ME) is described to take account of both robustness and embedding efficiency, which has a fair chance to recover the secret message when stego object is partly damaged. The approximated robustness–efficiency bound is derived to reveal the contradiction between robustness and embedding efficiency. Then, based on the parity‐check concatenation of systematic convolutional codes and syndrome‐trellis codes (STCs), we propose the practical damage‐resistance STCs (DR‐STCs). Experimental results show that DR‐STCs are near‐optimal DR‐ME schemes, which can approach the derived upper bound. Copyright © 2014 John Wiley & Sons, Ltd.

Defining embedding distortion for motion vector-based video steganography

This paper presents an effective methodology for motion vector-based video steganography. The main principle is to design a suitable distortion function expressing the embedding impact on motion vectors by exploiting the spatial-temporal correlation based on the framework of minimal-distortion steganography. Two factors are considered in the proposed distortion function, which are the statistical distribution change (SDC) of motion vectors in spatial-temporal domain and the prediction error change (PEC) caused by modifying the motion vectors. The practical embedding algorithm is implemented using syndrome-trellis codes (STCs). Experimental results show that the proposed method can enhance the security performance significantly compared with other existing motion vector-based video steganographic approaches, while obtaining the higher video coding quality as well.

Syndrome trellis codes based on minimal span generator matrix

To improve the embedding efficiency of steganography, syndrome coding based on the coding theory has attracted many researchers’ attentions. In this paper, we make use of the relationship between syndrome coding for minimizing additive distortion and maximum likelihood decoding for linear codes to analyze the main parameters of convolutional codes which influence the embedding efficiency. And, the new syndrome trellis codes based on minimal span generator matrix is proposed. It can be considered an alternative construction of the state-of-the-art syndrome trellis codes (STCs) proposed by Filler and Fridrich recently. Experimental results show that the proposed scheme owns the same embedding performance to STCs and achieve the reduced time complexity and storage requirement meanwhile.

A JPEG Steganographic Method Based on Syndrome-Trellis Codes with Wetness-Scale

Aiming at the JSTC’s shortcomings, we present an improved JPEG Steganographic method using Syndrome-Trellis Codes with Wetness-Scale in this paper. Firstly, the proposed method excludes the DC coefficients from usable coefficients for information embedding; secondly, when using Syndrome-Trellis Codes to modulate the message bits, it assigns each individual cover element with a special changing cost value provided by our predefined embedding impact model (i.e. wetness-scale), and endeavors to constrain the embedding changes to the low-frequency and middle-frequency DCT coefficients and minimize the embedding impact. The experimental results show that in comparison with JSTC and Jsteg 、F5、MB, the proposed method has stronger ability of anti-steganalysis, and especially when at the embedding rate 0.1, its highest detection accuracy by one of the current best bind steganalysis methods only achieves at 0.5435.

Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the nonbinary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolutional codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in steganographic applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.