Color Image Steganalysis Based on Residuals of Channel Differences

This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate that, for WOW and S-UNIWARD steganography, the improved features clearly decreased the average test errors of the existing features, and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms. Specifically, when the payload is smaller than 0.2 bpc, the average test error decreases achieve 4% and 3%.     

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images.     

A MV-Based Steganographic Algorithm for H.264/AVC without Distortion

H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography. In this paper, a novel motion vector (MV) based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion. Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm. In the embedding block, the motion vector space encoding is used to embed a (2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block. Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding. The re-motion compensation (re-MC) block will re-construct the macroblock (MB) whose motion vectors have been modified by embedding block. System block plays the role of the generator for chaotic sequences and encryptor for secret data. Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion, it also presents good undetectability for existing MV-based steganalysis feature. Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.     

Suitability of VVC and HEVC for Video Telehealth Systems

Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and findings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos. VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality. We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality. Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.     

Coverless Image Steganography Based on Jigsaw Puzzle Image Generation

Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels, creating the stego image. However, the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload. A coverless data hiding concept is proposed to solve this issue. Coverless does not mean that cover is not required, or the payload can be transmitted without a cover. Instead, the payload is embedded by cover generation or a secret message mapping between the cover file and the payload. In this paper, a new coverless image steganography method has been proposed based on the jigsaw puzzle image generation driven by a secret message. Firstly, the image is divided into equal rows then further divided into equal columns, creating blocks (i.e., sub-images). Then, according to secret message bits and a proposed mapping function, each block will have tabs/blanks to get the shape of a puzzle piece creating a fully shaped jigsaw puzzle stego-image. After that, the generated jigsaw puzzle image is sent to the receiver. Experimental results and analysis show a good performance in the hiding capacity, security, and robustness compared with existing coverless image steganography methods.     

Digital video steganalysis using motion vector recovery-based features

As a novel digital video steganography, the motion vector (MV)-based steganographic algorithm leverages the MVs as the information carriers to hide the secret messages. The existing steganalyzers based on the statistical characteristics of the spatial/frequency coefficients of the video frames cannot attack the MV-based steganography. In order to detect the presence of information hidden in the MVs of video streams, we design a novel MV recovery algorithm and propose the calibration distance histogram-based statistical features for steganalysis. The support vector machine (SVM) is trained with the proposed features and used as the steganalyzer. Experimental results demonstrate that the proposed steganalyzer can effectively detect the presence of hidden messages and outperform others by the significant improvements in detection accuracy even with low embedding rates.     

基于L1范数优化路径的视频稳像算法

目的 为解决外包装行业对产品外观进行检测时,采集视频图像存在抖动失真的问题.方法 提出一种基于L1范数优化路径的视频稳像算法,采用SURF算法和FREAK算法对视频序列帧中的特征点进行检测和描述;然后,使用KNN算法和RANSAC算法对相邻帧间的特征点进行匹配和筛选;最后,通过L1范数优化算法对序列帧进行校正和去黑边处理,得到稳像视频.结果 在处理前景无运动和前景有运动的2类视频时,稳像前后视频的平均PSNR值分别提升了5.094 dB和4.273 dB,并且相对于常用的特征匹配算法,文中算法能显著降低相机路径的主动平滑因子.结论 文中算法能够有效地解决视频抖动失真的问题,提高抖动视频的质量,具有一定的鲁棒性.     

Steganalysis of images based on spatial domain and two-dimensional JPEG array

The aim of digital image steganalysis is to detect hidden information (which can be a message or an image) in a steganographic image. An ideal steganography method encrypts the information in the image such that it cannot be easily detected. Currently, a wide variety of different steganography techniques are being used; therefore, more advanced steganalysis methods are needed that can detect the steganographic images coded by different techniques. A typical steganalysis technique consists of two parts: (1) feature extraction and (2) classification. In this paper, a new steganalysis technique based on the Markov chain process is proposed. In the proposed technique, after extraction of the new features, a non-linear classifier named support vector machine is applied to classify clean and encrypted images. Analysis of variance is used to reduce the dimensions of the proposed features. The performance of the proposed technique is compared against subtractive DCT coefficient adjacency matrix (SDAM) and subtractive pixel adjacency matrix (SPAM) methods using an image database prepared by three strong steganography techniques called yet another steganographic scheme, model based, and perturbed quantization. The obtained results show that the proposed method provides better performance than SDAM and SPAM methods.     

Varying index varying bits substitution algorithm for the implementation of VLSB steganography

Variable least significant bits (VLSB) steganography is a pretty powerful and secure technique for data hiding in cover images, having variable data hiding capacity, signal-to-noise ratio, peak signal-to-noise ratio, and mean square error (MSE). This study presents a new algorithm for the implementation of VLSB steganography named varying index varying bits substitution (VIVBS). The VIVBS algorithm is a very secure, high capacity, flexible, and statistically unpredictable mechanism to conceal information in cover images. The method uses a secret stego-key comprising a reference point, and variation of the number of bits to be hidden with varying indices of pixels in the cover image. The secret key adds an extra feature of security to steganography, making it much immune to steganalysis. The VIVBS algorithm is capable of providing variable data hiding capacity and variable key size which can be changed by changing the range of least significant bits used. A data hiding capacity of 43.75% with a negligible MSE 14.67 dB has been achieved using the VIVBS algorithm. For larger data hiding capacity, the MSE and distortion increases significantly which make the existence of information predictable but the key size also increases significantly, making the retrieval of hidden information difficult for the unauthorized person.     

An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters

Local binary pattern (LBP) is one of the most advanced image classification recognition operators and is commonly used in texture detection area. Research indicates that LBP also has a good application prospect in steganalysis. However, the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit (LSB) and the least significant bit matching (LSBM) algorithms. To solve this problem, this paper proposes a steganalysis model called msdeLTP, which is based on multi-scale local ternary patterns (LTP) and derivative filters. The main characteristics of the msdeLTP are as follows: First, to reduce the interference of image content on features, the msdeLTP uses derivative filters to acquire residual images on which subsequent operations are based. Second, instead of LBP features, LTP features are extracted considering that the LTP feature can exhibit multiple variations in the relationship of adjacent pixels. Third, LTP features with multiple scales and modes are combined to show the relationship of neighbor pixels within different radius and along different directions. Analysis and simulation show that the msdeLTP uses only 2592-dimensional features and has similar detection accuracy as the spatial rich model (SRM) at the same time, showing the high steganalysis efficiency of the method.     

Defining Embedding Distortion for Intra Prediction Mode-Based Video Steganography

In this paper, an effective intra prediction mode-based video strganography is proposed. Secret messages are embedded during the intra prediction of the video encoding without causing large embedding impact. The influence on the sum of absolute difference (SAD) in intra prediction modes (IPMs) reversion phenomenon is sharp when modifying IPMs. It inspires us to take the SAD prediction deviation (SPD) to define the distortion function. What is more, the mapping rule between IPMs and the codewords is introduced to further reduce the SPD values of each intra block. Syndrome-trellis code (STC) is used as the practical embedding implementation. Experimental results demonstrate that our proposed steganographic scheme presents high undetectability compared with existing IPMs-based steganographic approaches. It also outperforms these schemes on stego video quality.     

基于编码模式的H.264/AVC视频信息隐藏算法

本文提出一种基于编码模式的H.264/AVC信息隐藏方法,通过调制某些宏块的编码模式,分别在Ⅰ帧、P帧和B帧中嵌入隐秘信息.对帧内4x4预测模式的宏块,是通过调整宏块中某个4×4块的编码模式嵌入隐秘信息;对P帧和B帧其它类型的宏块,则是通过调整宏块的编码模式嵌入隐秘信息,模式调整后对宏块做了优化处理.在模式调制过程中引入率失真代价,取得了较好的率失真平衡,减小了隐秘信息嵌入后对视频质量和视频码流的影响.该算法可以实现隐秘信息的快速提取,满足视频实时处理的要求,实验仿真结果证明了该算法的有效性.     

Real-time and MPEG-1 layer III compression resistant steganography in speech

Embedding a secret message into a cover media without attracting any attention, known as steganography, is one of the methods used for hidden communication purposes. One of the cover media that can be used for steganography is speech. In this study, the authors propose a new steganography method in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. The main feature of our method is robustness to MPEG-1 layer III (MP3) compression. This method can hide information in a speech stream with very low processing time which makes it a real-time steganography method. The hiding capacity of our method is comparable with other MP3 resistance methods and the listening tests show that the degradation in speech quality is not annoying. Additionally, the effect of our method on chaotic features is negligible, so it is difficult to detect our method with chaotic-based steganalysis methods.     

Deep Learning Approach for Cosmetic Product Detection and Classification

As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks, namely ResNet, InceptionResNetV2, DenseNet, and EfficientNet, and compare their performance. Furthermore, we employ dilated convolution in these networks to obtain better feature representations and improve performance. Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy. Moreover, the dilated networks marginally outperform the base models, or achieve similar performance in the worst case. We conclude that the proposed method can effectively detect and classify cosmetic products.     

Objective analysis and bit rate analysis of HEVC compressed 4D-fMRI images

Medical imaging and clinical diagnostics are complementary to one another since their analysis is typical and contains critical information. The growing volume of data has become one of the biggest challenges, as the acquisition of medical modalities is currently having high resolution from the improved and efficient machines (3 to 7 T or more). Moreover, image and video compression is a need with the consideration that there should not be any gap for losing the important information. Less bitrate requirement with high compression ratio without sacrificing important detail is a challenge these days. The current study, is dealing with the compression of 4D-functional medical resonance images (fMRI) with a codec, that is, high-efficient video coding (HEVC/H.265) and its objective analysis along with its predecessor that is advanced video coding (AVC/H.264) and with VP8 (WebM Project of Google) reported here. Further, the bit rate analysis that has been conducted, also accounts in conjunction with the bitrate investigation, which is an imperative perspective vital for the telemedicine field. The simulation results reported here represents the compression ratio (CR = 118.23:1) with HEVC/H.265 codec over the compression ratio (CR = 20.52:1) provided by AVC/H.264 and VP8 (CR = 78.29:1). There has been significant improvement observed in alignment of the peak signal-to-noise ratio (APSNR), structural similarity (SSIM), and mean squared error (MSE) metrics. Overall, the performance of the anticipated technique is satisfactory for the forthcoming telemedicine or clinical use.     

Utilization of HEVC ChaCha20-Based Selective Encryption for Secure Telehealth Video Conferencing

Coronavirus (COVID-19) is a contagious disease that causes exceptional effect on healthcare organizations worldwide with dangerous impact on medical services within the hospitals. Because of the fast spread of COVID-19, the healthcare facilities could be a big source of disease infection. So, healthcare video consultations should be used to decrease face-to-face communication between clinician and patients. Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for face-to-face contact with a potentially positive patient without symptoms. These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely, patients who consult about COVID-19, and those who have mild symptoms suggestive of the COVID-19 virus. Video consultations are a supplement to, and not a substitute for, telephone consultations. It may also form part of a broader COVID-19 distance care strategy that contains computerized screening, separation of possibly infectious patients within medical services, and computerized video-intensive observing of their intensive care that helps reduce mixing. Nowadays, the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations, save costs and health procedures used to cope with the pandemic of the COVID-19 virus, and monitor the progress of treatment plans, moment by moment from a distance with precision, clarity and ease. From this perspective, this paper introduces a high-efficiency video coding (HEVC) ChaCha20-based selective encryption (SE) scheme for secure healthcare video Consultations. The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform (DCT) and Motion Vector Difference (MVD) in the HEVC entropy phase. The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time, fixed bit rate of the HEVC, and format compliance. Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet.     

An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis. Therefore, a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH. The contributions of this paper could be summarized as follows. (1) By analyzing the characteristics for those adaptive HS-RDH, an effective “flat ground” based detection method is designed to fast identify whether the given image is used to hide secret data; (2) According to the empirical statistical model, double check mechanism is provided to improve the detection accuracy; (3) In addition, to further improve detection ability, some detailed information for secret data, i.e., its content and embedding location are further estimated. Compared with conventional steganalysis methods, experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.     

Fast intra prediction mode decision for H.264/AVC video coding

Abstract

Intra coding is used for reducing the spatial redundancy in video coding. H.264 supports several macroblocks of predictions for intra coding such as luma block four 16×16 modes, nine 4×4 modes and chroma block four modes, which significantly improve intra coding efficiency, but increase the encoding complexity. In order to select the best mode, we need to calculate the cost of the various modes. In this paper, a fast intra prediction mode decision for H.264/AVC video coding is proposed. Based on Laplacian, this intra prediction mode decision detects edges and selects the best mode for the block. This mode decision can shorten the time to reduce the encoding time. The experimental results show that the proposed algorithm achieves an encoding time saving of 70% on average.     

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.     

Grand alliance MPEG-2-based video decoder with parallel processing architecture

A practical and unique hardware architecture for video bitstream source decoding and video postprocessing of a Moving Pictures Expert Group (MPEG-2)-based high-definition television (HDTV) compressed bitstream has been implemented to impose minimal limitations on the video source coding algorithm. The Grand Alliance (GA) MPEG-2-based HDTV codec achieves a high degree of source and channel coding efficiency while preserving the delivery of high-resoultion picture quality in a variety of video input and output formats in bandwidth-limited channels. The video source decoder hardware architecture necessary to achieve the data decoding and ensuing video postprocessing poses numerous technologic challenges to the system designer, who must tradeoff minimizing codec constraints with the eventual commercialization of a video decoder for a consumer television receiver product. The powerful and flexible coding algorithm necessary to satisfy the HDTV picture quality and transmission channel bandwidth limitation requirements results in an encoder-output bitstream that necessitates high throughout decoding. Although the transmitted bitstream is of constant rate due to rate buffering, bistreams internal to the codec are both peaky and bursty. An intelligent distributive parallel processing decoding architecture has been developed to dynamically partition the MPEG-2 bitstream into a number of decodable subset bitstreams, while placing minimal constraints on the encoding algorithm. This architecture allows for high-speed, efficient decoding of the bitstream, and can be a prelude to the development of a cost-effective consumer product. Further architecture refinements can be explored, including implementation in VLSI.