Syndrome-trellis Codes Research Articles

Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion.

Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the Q-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the Q-layered STC, given the variation of Q, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.

Errorless robust JPEG steganography using steganographic polar codes

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients. In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding. We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC. Specifically, when the quality factor of the compressor is 95 and the payload size is 0.4 bpnzac, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC.

Adaptive HEVC video steganograhpy based on PU partition modes

High EfficiencyVideoCoding (HEVC) −based steganography has gained attention as a prominent research focus. Especially, block structure based HEVC video steganography has received increasing attention due to commendable performance. However, current block structure- based steganography algorithms confront with challenges such as reduced coding efficiency and limited capacity. To avoid these problems, an adaptive video steganography algorithm based on Prediction Unit (PU) partition mode in I-frames is proposed. This is done through the analysis of the block division process and the visual distortion resulting from the modification of the PU partition mode in HEVC. The PU block structure is utilized as steganographic covers, and the Rate Distortion Optimization (RDO) technique is introduced to establish an adaptive distortion function for Syndrome-trellis code (STC). Further comparison is performed between the proposed method and the state-of-the-art steganography algorithms, confirming its advantages in embedding capacity, compression efficiency, visual quality, and resistance to video steganalysis.

Adaptive HEVC Video Steganography With High Performance Based on Attention-Net and PU Partition Modes

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.

Extraction Method of Secret Message Based on Optimal Hypothesis Test

As the ultimate goal of steganalysis, secret message extraction plays a decisive role in obtaining secret communication evidence and cracking down on criminal activities. For STC (Syndrome-Trellis Codes)-based adaptive steganography, existing pioneering work on secret message extraction: the method based on run test under plaintext embedding may misjudge incorrect stego key as correct stego key, resulting in the failure of extraction. To avoid such a situation, this manuscript proposed a secret message extraction method based on optimal hypothesis test with 100% accuracy under plaintext embedding. First, it is proved that there is a probability distribution difference between the sub-sequence extracted by correct and incorrect stego key. Then, based on the difference, an optimal hypothesis test model is designed to recover the correct stego key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method.

An Adaptive IPM-Based HEVC Video Steganography via Minimizing Non-Additive Distortion

Recently, almost all the proposed adaptive video steganographic schemes are based on minimizing an additive embedding distortion. However, they ignore the hard fact that the additive embedding distortion is not quite suitable for video steganography because of the interplay of cover elements in video steganography. In this paper, an adaptive intra prediction mode based (IPM-based) video steganography is proposed by minimizing the non-additive distortion in HEVC. To reduce the complexity of minimizing the non-additive distortion, a multi-layered embedding structure combined with a proposed embed-ding distortion updating strategy is adopted to approximate the non-additive distortion in an additive form. Firstly, all IPMs are decomposed into multiple layers based on the distortion drift graph to offer multi-layered embedding. Each IPM in the same layer is considered to be independent, and syndrome-trellis code (STC) can be applied to embed the message segment into each layer with an additive distortion function sequentially. Then, a distortion function composed of self-distortion and drift-distortion is proposed to initialize the distortion of modifying each IPM. Finally, after embedding the first message segment into the IPMs in the first layer with the initialized distortions, an embedding distortion updating strategy is applied to update the distortions of the IPMs in the remaining layers dynamically. Ex-perimental results demonstrate that the proposed adaptive IPM-based video steganography can achieve much better perceptual quality and security performance than the state-of-the-art.

Adaptive HEVC video steganography based on distortion compensation optimization

With the development of digital video communication, the steganography based on H.265/HEVC has attracted more and more attention. Because of higher security and better compatibility, the traditional video steganography often used quantized discrete cosine transform (QDCT) or quantized discrete sine transform (QDST) coefficients to embed secret messages. However, the QDCT/QDST-based steganographic methods will cause distortion drift seriously. Although the distortion compensation optimization method can eliminate the intra-frame distortion drift, it ignored the influence of inter-frame distortion. This limitation prompts authors to propose an adaptive HEVC steganography, which eliminates the intra-frame distortion drift while reducing inter-frame distortion. First, the distortion of intra-block and inter-block is analyzed with the prediction and transform process. Then, the corresponding embedding influence is defined, which includes intra-block distortion, inter-frame distortion accumulation, the block texture complexity and the cost of coefficient groups. Finally, the proposed adaptive cost function is designed based on the above definition and the syndrome trellis codes (STC) is used for embedding. The experimental results show that the proposed method has better performance compared with the state-of-the-art coefficient-based steganography in visual quality, bit rate increment and anti-steganalysis.

Advanced audio coding steganography algorithm with distortion minimization model based on audio beat

Currently, most advanced audio coding (AAC) steganography methods are content-non-adaptive without considering the characteristics of audio, and there are several limitations in imperceptibility and steganalysis. In this paper, we use an audio feature beat as the anchor point to identify the cover elements, group the quantized modified discrete cosine transform (QMDCT) coefficients in the small value area, and finally use the syndrome-trellis codes (STCs) framework for content-adaptive embedding to obtain the minimum distortion. In the STCs framework, we comprehensively consider auditory and data distortions. Experimental results demonstrate that the proposed steganography algorithm has a 10% improvement over the compared algorithms in terms of imperceptibility and steganalysis, and it can accurately extract secret information in face of frame loss and misalignment.

Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.

HEVC video information hiding scheme based on adaptive double-layer embedding strategy

High Efficiency Video Coding (HEVC) is well-known as an internationally popular video coding standard, and HEVC-based steganography has received increasing attention. In this paper, a new adaptive HEVC video information hiding method based on Prediction Unit (PU) partition mode and double-layer embedding strategy is proposed. Double-layer embedding is a method to complete the first-layer embedding using the mapping rules of PU partition mode, and to perform the second-layer embedding after the first-layer embedding. The cost assignment function designed in this paper can accurately evaluate the second-layer data embedding distortion. The frame position, motion properties and block size of PU are taken into consideration for the second-layer data embedding, and the syndrome-trellis codes (STCs) are used to minimize the embedding distortion. Experimental results show that the proposed adaptive double-layer embedding algorithm has better embedding efficiency and less embedding distortion in most cases.

Secure medical image steganography based on Discrete Wavelet Transformation and ElGamal encryption algorithm

The latest development in computing methodologies and related steganography based applications had created a novel practice such as telemedicine where patient diagnosis images or allied information can be used diagnosis practices that are located in remote areas. In order to provide accurate and effective telemedicine the flawless or seamless biomedical information is required from patient. With respect to this the medical data may remain prone to get corrupted by hackers or might get manipulated when transmitted through an insecure channel. The cryptosystems which are available are not efficient enough to solve the above mentioned issues. Hence, in this research work a much efficient and effective image steganography technique has been proposed in order to hide the confidential and important image information. We have incorporated Discrete Wavelet Transformation (DWT) technique instead of wavelet transform techniques to embed the secret message in the required cover images. Also, to assure continuous communication through insecure channel, a new model called ElGamal encryption cryptosystem model has been implemented which contains the steganography scheme which has been proposed and developed. To maintain the content of Region of Interest (ROI), Region of Non- Interest (RONI) is used to embed the secret information. The overall performance analysis reveals that the DWT with ElGamal encryption provides more efficient and also have high embedding capacity, imperceptibility when compared to other methods like Hamming code, Syndrome-Trellis Code (STC) and Rivest-Shamir- Adleman (RSA) based methods.

Toward Steganographic Payload Location via Neighboring Weight Algorithm

Modern steganalysis has been widely investigated, most of which mainly focus on dealing with the problem of detecting whether an inquiry image contains hidden information. However, few articles in the literature study the location of secret bits hidden by modern adaptive steganography. In this paper, we propose a novel algorithm for locating steganographic payload in the spatial domain. We first predict the steganographic scheme and its payload, which is used for generating a random bitstream. Then, the random bits are embedded in the stego image based on the cost matrix in the framework of Syndrome-Trellis Codes (STCs). Next, relying on the differences between two stego images, the extended modification map in couple with the neighboring weight algorithm can be acquired, leading to the location of the hidden bits. Compared with the prior art, the extensive experiments verify that our proposed locating algorithm performs better, in terms of locating accuracy and efficiency.

Cryptographic Secrecy Analysis of Adaptive Steganographic Syndrome-Trellis Codes

Compared with traditional steganography, adaptive steganography based on STC (Syndrome-Trellis Codes) has extremely high antidetection ability and has been a mainstream and hot research direction in the field of information hiding over the past decades. However, it is noted, in specific scenarios, that a small number of methods can extract data from STC-based adaptive steganography, indicating security risks related to such algorithms. In this manuscript, the cryptographic secrecy of this kind of steganography is analyzed, on condition of two common attacks: stego-only attack and known-cover attack, respectively, from three perspectives: steganographic key equivocation, message equivocation, and unicity distance of the steganographic key. Focusing on the special layout characteristics of the parity-check matrix of STC, under the two attack conditions, the theoretical boundaries of the steganographic key equivocation function, the message equivocation function, and the unicity distance of the steganographic key are separately obtained, showing the impact of the three elements: the submatrix size, the randomness of the data, and the cover object on the cryptographic secrecy of the STC-based adaptive steganography, resulting in a theoretical reference to accurately judge the cryptographic secrecy of such steganography and design more secure steganography methods.

An Intra-Prediction Mode-Based Video Steganography With Secure Strategy

In this paper, an intra-prediction mode (IPM)-based video steganography with secure strategy was proposed for H.264 video stream. First of all, according to the property of IPM conversion after calibration, a content-adaptive selection strategy was adopted to measure candidate carrier macroblock. Then, a more efficient encoding strategy based on grouped IPM was applied to encode secret message. This encoding strategy aimed to further enhance the security performance by exploiting the deviation feature of calibrated IPM. Finally, syndrome-trellis code was used as the embedding implementation to minimize distortion. Experimental results demonstrate that this article proposed algorithm presents a novel security performance with any existing IPM-based video steganography.

Secure halftone image steganography based on density preserving and distortion fusion

Most state-of-the-art halftone image steganographic methods measure the flipping distortion according to the statistics of the image database but neglect the characteristic of a single image. In this paper, a halftone image steganography based on density preserving and distortion fusion is proposed for minimizing the flipping distortion. Different from ordinary binary images, halftone images express the texture information by the intensity of local regions that are represented by the pixel density. Based on the density distribution of a halftone image, the embedding costs defined by a single existing distortion function are modified adaptively to maintain the original texture structure as much as possible. Furthermore, to select more appropriate flipped pixels, a distortion fusion strategy is developed from the perspective of anti-steganographic analysis, which can be applied to a batch of distortion functions. Finally, the syndrome-trellis code (STC) is applied to minimize the embedding distortion for playing the advantage of the distortion measurement. Experimental results show that the proposed steganographic scheme achieves great visual quality and strong statistical security without degrading the embedding capacity.

Robust steganography resisting JPEG compression by improving selection of cover element

Through minimizing embedding cost, modern adaptive steganography has gained unprecedented success in terms of its undetectability. However, due to Joint Photographic Experts Group (JPEG) compression, the secret bits hidden in the image transmitted over social network platform fail to be perfectly extracted, that remarkably limits its wide application in the real world. In this paper, by improving selection of cover element, we propose to design an enhanced robust steganographic algorithm to resist against JPEG compression. First, since that before and after JPEG compression the sign of Discrete Cosine Transform (DCT) coefficient is not easy to change, we devise cover element based on the sign of DCT coefficient. Furthermore, the selection of cover element is successfully improved with the help of post-processing operation analysis. Second, the embedding cost for each candidate DCT coefficient is calculated. Finally, dependent of both error correction algorithm and syndrome-trellis codes, a compression-resistant stego image is generated with minimum distortion. Numerical results verify that the robustness of our proposed steganographic algorithm is superior to that of current arts; meanwhile, the effectiveness of the algorithm is also verified over social network platform.

DDCA: A Distortion Drift-Based Cost Assignment Method for Adaptive Video Steganography in the Transform Domain

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this article, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-Based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.

Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.

A New Adversarial Embedding Method for Enhancing Image Steganography

Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since adding perturbations to stegos may lead to messages extraction failure due to properties of syndrome-trellis codes (STC), the existing adversarial examples are derived from covers or their enhanced versions, while those stegos are not fully utilized. In this paper, we propose a new adversarial embedding scheme for image steganography. Unlike those related works, we first combine multiple gradients of cover and generated stegos to determine the directions of cost modifications. Next, instead of adjusting all or a random part of embedding costs in existing works, we carefully select the candidate costs according to the amplitudes of cover gradients and their costs. Extensive experimental results demonstrate that by adjusting a tiny part of embedding costs (less than 5% in most cases), the proposed method can significantly improve the security of five modern steganographic methods evaluated on both re-trained CNN-based and traditional steganalyzers, and achieve much better security performances compared with related methods. In addition, the security performances evaluated on different image database show that the generalization of the proposed method is good.

Secure Halftone Image Steganography Based on Feature Space and Layer Embedding.

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.