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.     

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.     

Performance improvement of LSB-based steganalysis using bit-plane decomposition of images

In this paper, we present an improved least significant bit (LSB)-based steganalysis scheme using the bit-plane decomposition of images. We derive a mathematical condition that can enhance the detection rate for hidden messages based on the correlation coefficient between two parts of a decomposed image. Based on this condition, images are classified and segregated into two groups: the full image including all of the bit-planes and a sub-image containing only the lower bit-planes. The feature vectors for steganalysis are extracted independently form each group. Three types of conventional feature vectors were extracted to verify our proposed method and experiments demonstrated that conventional steganalysis schemes exhibited improved performance using our proposed method. In conclusion, our scheme can be used as a general steganalyzer regardless of the specific steganalysis methods employed for LSB-based steganalysis.     

An Image Steganography Algorithm Based on Quantization Index Modulation Resisting Scaling Attacks and Statistical Detection

In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method, this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical detection. For the spatial image, this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded domain. Then construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography, and use the minimum distortion coding to realize the embedding of the secret messages. Finally, according to the embedding modification amplitude of secret messages in the new embedded domain, the quantization index modulation algorithm is applied to realize the final embedding of secret messages in the original embedded domain. The experimental results show that the algorithm proposed is robust to the three common interpolation attacks including the nearest neighbor interpolation, the bilinear interpolation and the bicubic interpolation. And the average correct extraction rate of embedded messages increases from 50% to over 93% after 0.5 times-fold scaling attack using the bicubic interpolation method, compared with the classical steganography algorithm S-UNIWARD. Also the algorithm proposed has higher detection resistance than the original watermarking algorithm based on quantization index modulation.     

A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

This paper proposes a two-step general framework for reversible data hiding (RDH) schemes with controllable contrast enhancement. The first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity (EC), while the second step is used for increasing image contrast. In the second step, some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization (HE), which would lead to the image contrast enhancement. However, for HE, the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are embedded. Therefore, in our proposed framework, contrast over-enhancement is avoided by controlling the degree of contrast enhancement. Since the second step can only provide a small amount of data due to controlled contrast enhancement, the first one helps to achieve a large amount of data without degrading visual quality. Any RDH method which can achieve high EC while preserve good visual quality, can be selected for the first step. In fact, Gao et al.’s method is a special case of our proposed framework. In addition, two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.     

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.     

Secret data embedding scheme modifying the frequency of occurrence of image brightness values

The main purpose of this presented work is to develop a data embedding method based on a new digital image histogram modification approach. The proposed scheme fundamentally is concerned about the frequency of occurrence of the image brightness values of the cover image for the data embedding procedures. The proposed scheme effectively realizes both perceptual invisibility and statistical invisibility so that obtained covered images are highly robust against common perceptual and statistical steganalysis techniques. The scheme provides reasonably higher payload values than its counterparts, as well as providing comparatively improved PSNR results.     

A high-payload,reversible data hiding scheme based on histogram modification in JPEG bitstream

In this paper, we have presented a high-payload, reversible data hiding scheme that is focused on embedding secret data into joint photographic experts group (JPEG) bitstream by using histogram modification. In JPEG, for AC coefficients, 162 different variable length codes (VLCs) are defined and assigned by Huffman coding for all 162 run/size possibilities. After parsing VLC usage in the bitstream, we generated a histogram of run/size. According to a peak point and the nearest zero point of the histogram, the secret data were embedded by modifying the corresponding VLC values slightly. The experimental results and comparing the performance of our scheme with those of other existing schemes demonstrated that the proposed scheme preserved the quality of the image with no distortion and achieved the goal of high embedding capacity.     

Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method

Robust data hiding techniques attempt to construct covert communication in a lossy public channel. Nowadays, the existing robust JPEG steganographic algorithms cannot overcome the side-information missing situation. Thus, this paper proposes a new robust JPEG steganographic algorithm based on the high tense region location method which needs no side-information of lossy channel. First, a tense region locating method is proposed based on the Harris-Laplacian feature point. Then, robust cover object generating processes are described. Last, the advanced embedding cost function is proposed. A series of experiments are conducted on various JPEG image sets and the results show that the proposed steganographic algorithm can resist JPEG compression efficiently with acceptable performance against steganalysis statistical detection libraries GFR (Gabor Filters Rich model) and DCTR (Discrete Cosine Transform Residual).     

DWT-based blind and robust watermarking using SPIHT algorithm with applications in tele-medicine

Malicious manipulation of digital images cannot be ignored in certain critical application like tele-medicine, where medical records of the patients (mostly in the form of diagnostic images of brain, chest, heart, lungs, etc.) need to be securely transmitted over the internet for various life-saving consultation and treatments. Watermarking is used to protect such documents from being manipulated and helps detect the authenticity of the image received. A robust and blind image watermarking technique based on discrete wavelet transform is proposed in this paper. For embedding purpose, suitable coefficients are found out using the Set Partitioning in Hierarchical Trees algorithm. Level of security is further enhanced by scrambling the watermark using Arnold transform and by making the set of embedding locations random using a secret key. Noise Visibility Function is used to compute the minimum allowable distortion for each coefficient before embedding to achieve better image fidelity. Experimental results show that the proposed scheme is robust against certain standard image processing attacks and outperforms the prior existing similar types of watermarking schemes.     

Improved security in the genetic algorithm-based image steganography scheme using Hilbert space-filling curve

This paper presents the security analysis of a recently proposed data hiding scheme by Kanan and Nazeri [A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl. 2014;41(14):6123–6130]. Security of the scheme depends upon a secret key\chromosome constructed using a genetic algorithm. The key specifies offsets and patterns for data embedding. By performing the key-sensitivity analysis and exploiting image pixel correlation, it is shown in the present paper that the secret data can be easily revealed. Simulation results demonstrate that just 11 bits out of the 27-bit secret key are sufficient to reveal the data. To overcome the problem, an improvement is suggested in the data embedding pattern that demonstrates high data security. Bitwise analysis of the secret key used in the modified approach exhibits high sensitivity to change in the key.     

USAD: undetectable steganographic approach in DCT domain

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.     

A reversible steganographic algorithm for BTC-compressed images based on difference expansion and median edge detector

In this paper, we propose a reversible steganographic algorithm for compressed images. The algorithm firstly compresses the input image using block truncation coding. One binary map and two quantisation levels, called high and low levels, are then obtained for each block. Thereafter, we adopt a median edge detector to predict the high and low quantisation levels for neighbouring blocks. A secret message is then embedded into the predicted difference based on the difference expansion technique. Each block can be classified as embeddable and non-embeddable according to the order of two quantisation levels. Thus, the location map is unnecessary in our proposed algorithm. The experimental results show that our data-embedded compressed code can be the same file size compared with standard block truncation coding-compressed code. Our algorithm can also resist the RS steganalysis attack. Further, the embedding capacity can be varied according to the given embedding parameter. The feasibility of our proposed algorithm is validated by presenting comparisons with existing algorithms.     

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.     

A HEVC Video Steganalysis Algorithm Based on PU Partition Modes

Steganalysis is a technique used for detecting the existence of secret information embedded into cover media such as images and videos. Currently, with the higher speed of the Internet, videos have become a kind of main methods for transferring information. The latest video coding standard High Efficiency Video Coding (HEVC) shows better coding performance compared with the H.264/AVC standard published in the previous time. Therefore, since the HEVC was published, HEVC videos have been widely used as carriers of hidden information.
In this paper, a steganalysis algorithm is proposed to detect the latest HEVC video steganography method which is based on the modification of Prediction Units (PU) partition modes. To detect the embedded data, All the PU partition modes are extracted from P pictures, and the probability of each PU partition mode in cover videos and stego videos is adopted as the classification feature. Furthermore, feature optimization is applied, that the 25-dimensional steganalysis feature has been reduced to the 3-dimensional feature. Then the Support Vector Machine (SVM) is used to identify stego videos. It is demonstrated in experimental results that the proposed steganalysis algorithm can effectively detect the stego videos, and much higher classification accuracy has been achieved compared with state-of-the-art work.     

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.     

An adaptive EWMA scheme‐based CUSUM accumulation error for efficient monitoring of process location

The examination of product characteristics using a statistical tool is an important step in a manufacturing environment to ensure product quality. Several methods are employed for maintaining product quality assurance. Quality control charts, which utilize statistical methods, are normally used to detect special causes. Shewhart control charts are popular; their only limitation is that they are effective in handling only large shifts. For handling small shifts, the cumulative sum (CUSUM) and the exponential weighted moving average (EWMA) are more practical. For handling both small and large shifts, adaptive control charts are used. In this study, we proposed a new adaptive EWMA scheme. This scheme is based on CUSUM accumulation error for detection of wide range of shifts in the process location. The CUSUM features in the proposed scheme help with identification of prior shifts. The proposed scheme uses Huber and Tukey bisquare functions for an efficient shift detection. We have used average run length (ARL) as performance indicator for comparison, and our proposed scheme outperformed some of the existing schemes. An example that uses real‐life data is also provided to demonstrate the implementation of the proposed scheme.     

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.     

Digital image watermarking based on VSS in BTC domain

Abstract

An image watermarking model based on visual secret sharing (VSS) is proposed for protection of ownership. In the embedding phase, the watermark is first divided by VSS into two parts: a hiding watermark and a secret watermark. Then only the hiding watermark is embedded into the original image and the secret watermark is reserved for watermark extracting by the owner. In the extracting phase, the hiding watermark is extracted from the watermarked image first and then directly superimposed on the secret watermark to recover the watermark information.     

Coverless Steganography for Digital Images Based on a Generative Model

In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information steganographic scheme based on generative model, compared with the traditional image steganography. The transmitted image is not embedded with any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our scheme has high capacity, security and reliability.