Biblio

With the development of social networks, traditional covert communication requires more consideration of lossy processes of Social Network Platforms (SNPs), which is called robust steganography. Since JPEG compression is a universal processing of SNPs, a method using repeated JPEG compression to fit transport channel matching is recently proposed and shows strong compression-resist performance. However, the repeated JPEG compression will inevitably introduce other artifacts into the stego image. Using only traditional steganalysis methods does not work well towards such robust steganography under low payload. In this paper, we propose a simple and effective method to detect the mentioned steganography by chasing both steganographic perturbations as well as continuous compression artifacts. We introduce compression-forensic features as a complement to steganalysis features, and then use the ensemble classifier for detection. Experiments demonstrate that this method owns a similar and better performance with respect to both traditional and neural-network-based steganalysis.

Minimizing embedding impact model of steganography has good performance for steganalysis detection. By using effective distortion cost function and coding method, steganography under this model becomes the mainstream embedding framework recently. In this paper, to improve the anti-detection performance, a new steganography optimization model by constructing a reference cover is proposed. First, a reference cover is construed by performing a filtering operation on the cover image. Then, by minimizing the residual between the reference cover and the original cover, the optimization function is formulated considering the effect of different modification directions. With correcting the distortion cost of +1 and \_1 modification operations, the stego image obtained by the proposed method is more consistent with the natural image. Finally, by applying the proposed framework to the cost function of the well-known HILL embedding, experimental results show that the anti-detection performance of the proposed method is better than the traditional method.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

Although the Pixel-Value Differencing (PVD) steganography can avoid being detected by the RS steganalysis, the histogram of the pixel-value differences poses an abnormal distribution. Based on this hiding characteristic, this paper proposes a PVD steganalysis based on chi-Square statistics. The degrees of freedom were adopted to be tested for obtaining various detection accuracies (ACs). Experimental results demonstrate the detection accuracies are all above 80%. When the degrees of freedom are set as 10 while the accuracy is the best (AC = 83%). It means that the proposed Chi-Square based method is an efficient detection for PVD steganography.

Many portable imaging devices use the operation of "trunc" (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source. Steganalysis detectors need to be retrained since a steganalyst unaware of the existence of the trunc quantizer will experience 100% false alarm.

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

Steganography is one of the techniques for secure transformation of data which aims at hiding information inside other media in such a way that no one will notice. The cover media that can accommodate secret information include text, audio, image, and video. Images are the most popular covering media in steganography, due to the fact that, they are heavily used in daily applications and have high redundancy in representation. In this paper, we propose an adaptive steganography algorithm for hiding information in RGB images. To minimize visual perceptible distortion, the proposed algorithm uses edge pixels for embedding data. It detects the edge pixels in the image using the Sobel filter. Then, the message is embedded into the LSBs of the blue channel of the edge pixels. To resist statistical attacks, the distribution of the blue channel of the edge pixels is used when embedding data in the cover image. The experimental results showed that the algorithm offers high capacity for hiding data in cover images; it does not distort the quality of the stego image; it is robust enough against statistical attacks; and its execution time is short enough for online data transfer. Also, the results showed that the proposed algorithm outperforms similar approaches in all evaluation metrics.

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.

Steganalysis is an interesting classification problem in order to discriminate the images, including hidden messages from the clean ones. There are many methods, including deep CNN networks to extract fine features for this classification task. Nevertheless, a few researches have been conducted to improve the final classifier. Some state-of-the-art methods try to ensemble the networks by a voting strategy to achieve more stable performance. In this paper, a selection phase is proposed to filter improper networks before any voting. This filtering is done by a binary relevance multi-label classification approach. The Logistic Regression (LR) is chosen here as the last layer of network for classification. The large-margin Fisher’s linear discriminant (FLD) classifier is assigned to each one of the networks. It learns to discriminate the training instances which associated network is suitable for or not. Xu-Net, one of the most famous state-of-the-art Steganalysis models, is chosen as the base networks. The proposed method with different approaches is applied on the BOSSbase dataset and is compared with traditional voting and also some state-of-the-art related ensemble techniques. The results show significant accuracy improvement of the proposed method in comparison with others.

Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.

In recent years, various cloud-based services have been introduced in our daily lives, and information security is now an important topic for protecting the users. In the literature, many technologies have been proposed and incorporated into different services. Data hiding or steganography is a data protection technology, and images are often used as the cover data. On the other hand, steganalysis is an important tool to test the security strength of a steganography technique. So far, steganalysis has been used mainly for detecting the existence of secret data given an image, i.e., to classify if the given image is a normal or a stego image. In this paper, we investigate the possibility of identifying the locations of the embedded data if the a given image is suspected to be a stego image. The purpose is of two folds. First, we would like to confirm the decision made by the first level steganalysis; and the second is to provide a way to guess the size of the embedded data. Our experimental results show that in most cases the embedding positions can be detected. This result can be useful for developing more secure steganography technologies.

This paper presents an effective steganalytic scheme based on CNN for detecting MP3 steganography in the entropy code domain. These steganographic methods hide secret messages into the compressed audio stream through Huffman code substitution, which usually achieve high capacity, good security and low computational complexity. First, unlike most previous CNN based steganalytic methods, the quantified modified DCT (QMDCT) coefficients matrix is selected as the input data of the proposed network. Second, a high pass filter is used to extract the residual signal, and suppress the content itself, so that the network is more sensitive to the subtle alteration introduced by the data hiding methods. Third, the \$ 1 $\backslash$times 1 \$ convolutional kernel and the batch normalization layer are applied to decrease the danger of overfitting and accelerate the convergence of the back-propagation. In addition, the performance of the network is optimized via fine-tuning the architecture. The experiments demonstrate that the proposed CNN performs far better than the traditional handcrafted features. In particular, the network has a good performance for the detection of an adaptive MP3 steganography algorithm, equal length entropy codes substitution (EECS) algorithm which is hard to detect through conventional handcrafted features. The network can be applied to various bitrates and relative payloads seamlessly. Last but not the least, a sliding window method is proposed to steganalyze audios of arbitrary size.

The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses a ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather then using high performance computing and machine learning. By the double embedding attack on the well-known F5 steganographic algorithm we achieve a false positive rate well below known attacks.

The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses an ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather then using high performance computing and machine learning. To achieve this we first analyze the steganographic algorithm F5 applied to images with a high degree of diversity, as would be seen in a typical social network. We show that the biggest challenge lies in the detection of images whose payload is less then 50% of the available capacity of an image. We suggest new detection methods and apply these to the problem of channel detection in social network. We are able to show that using our attacks we are able to detect the majority of covert F5 channels after a mix containing 10 stego images has been classified by our scheme.

Deep neural network based steganalysis has developed rapidly in recent years, which poses a challenge to the security of steganography. However, there is no steganography method that can effectively resist the neural networks for steganalysis at present. In this paper, we propose a new strategy that constructs enhanced covers against neural networks with the technique of adversarial examples. The enhanced covers and their corresponding stegos are most likely to be judged as covers by the networks. Besides, we use both deep neural network based steganalysis and high-dimensional feature classifiers to evaluate the performance of steganography and propose a new comprehensive security criterion. We also make a tradeoff between the two analysis systems and improve the comprehensive security. The effectiveness of the proposed scheme is verified with the evidence obtained from the experiments on the BOSSbase using the steganography algorithm of WOW and popular steganalyzers with rich models and three state-of-the-art neural networks.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

3D steganography is used in order to embed or hide information into 3D objects without causing visible or machine detectable modifications. In this paper we rethink about a high capacity 3D steganography based on the Hamiltonian path quantization, and increase its resistance to steganalysis. We analyze the parameters that may influence the distortion of a 3D shape as well as the resistance of the steganography to 3D steganalysis. According to the experimental results, the proposed high capacity 3D steganographic method has an increased resistance to steganalysis.

In this paper, we propose a method for normalization of rich feature sets to improve detection accuracy of simple classifiers in steganalysis. It consists of two steps: 1) replacing random subsets of empirical joint probability mass functions (co-occurrences) by their conditional probabilities and 2) applying a non-linear normalization to each element of the feature vector by forcing its marginal distribution over covers to be uniform. We call the first step random conditioning and the second step feature uniformization. When applied to maxSRMd2 features in combination with simple classifiers, we observe a gain in detection accuracy across all tested stego algorithms and payloads. For better insight, we investigate the gain for two image formats. The proposed normalization has a very low computational complexity and does not require any feedback from the stego class.

There have been a growing number of interests in using the convolutional neural network(CNN) in image forensics, where some excellent methods have been proposed. Training the randomly initialized model from scratch needs a big amount of training data and computational time. To solve this issue, we present a new method of training an image forensic model using prior knowledge transferred from the existing steganalysis model. We also find out that CNN models tend to show poor performance when tested on a different database. With knowledge transfer, we are able to easily train an excellent model for a new database with a small amount of training data from the new database. Performance of our models are evaluated on Bossbase and BOW by detecting five forensic types, including median filtering, resampling, JPEG compression, contrast enhancement and additive Gaussian noise. Through a series of experiments, we demonstrate that our proposed method is very effective in two scenario mentioned above, and our method based on transfer learning can greatly accelerate the convergence of CNN model. The results of these experiments show that our proposed method can detect five different manipulations with an average accuracy of 97.36%.

This article deals with color images steganalysis based on machine learning. The proposed approach enriches the features from the Color Rich Model by adding new features obtained by applying steerable Gaussian filters and then computing the co-occurrence of pixel pairs. Adding these new features to those obtained from Color-Rich Models allows us to increase the detectability of hidden messages in color images. The Gaussian filters are angled in different directions to precisely compute the tangent of the gradient vector. Then, the gradient magnitude and the derivative of this tangent direction are estimated. This refined method of estimation enables us to unearth the minor changes that have occurred in the image when a message is embedded. The efficiency of the proposed framework is demonstrated on three stenographic algorithms designed to hide messages in images: S-UNIWARD, WOW, and Synch-HILL. Each algorithm is tested using different payload sizes. The proposed approach is compared to three color image steganalysis methods based on computation features and Ensemble Classifier classification: the Spatial Color Rich Model, the CFA-aware Rich Model and the RGB Geometric Color Rich Model.

The MP4 files has become to most used video media file available, and will mostly likely remain at the top for some time to come. This makes MP4 files an interesting candidate for steganography. With its size and structure, it offers a challenge to steganography developers. While some attempts have been made to create a truly covert file, few are as successful as Martin Fiedler's TCSteg. TCSteg allows users to hide a TrueCrypt hidden volume in an MP4 file. The structure of the file makes it difficult to identify that a volume exists. In our analysis of TCSteg, we will show how Fielder's code works and how we may be able to detect the existence of steganography. We will then implement these methods in hope that other steganography analysis can use them to determine if an MP4 file is a carrier file. Finally, we will address the future of MP4 steganography.

Steganography is the science of hiding data within data. Either for the good purpose of secret communication or for the bad intention of leaking sensitive confidential data or embedding malicious code or URL. However, many different carrier file formats can be used to hide these data (network, audio, image..etc) but the most common steganography carrier is embedding secret data within images as it is considered to be the best and easiest way to hide all types of files (secret files) within an image using different formats (another image, text, video, virus, URL..etc). To the human eye, the changes in the image appearance with the hidden data can be imperceptible. In fact, images can be more than what we see with our eyes. Therefore, many solutions where proposed to help in detecting these hidden data but each solution have their own strong and weak points either by the limitation of resolving one type of image along with specific hiding technique and or most likely without extracting the hidden data. This paper intends to propose a novel detection approach that will concentrate on detecting any kind of hidden URL in all types of images and extract the hidden URL from the carrier image that used the LSB least significant bit hiding technique.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.

There are more and more systems using mobile devices to perform sensing tasks, but these increase the risk of leakage of personal privacy and data. Data hiding is one of the important ways for information security. Even though many data hiding algorithms have worked on providing more hiding capacity or higher PSNR, there are few algorithms that can control PSNR effectively while ensuring hiding capacity. In this paper, with controllable PSNR based on LSBs substitution- PSNR-Controllable Data Hiding (PCDH), we first propose a novel encoding plan for data hiding. In PCDH, we use the remainder algorithm to calculate the hidden information, and hide the secret information in the last x LSBs of every pixel. Theoretical proof shows that this method can control the variation of stego image from cover image, and control PSNR by adjusting parameters in the remainder calculation. Then, we design the encoding and decoding algorithms with low computation complexity. Experimental results show that PCDH can control the PSNR in a given range while ensuring high hiding capacity. In addition, it can resist well some steganalysis. Compared to other algorithms, PCDH achieves better tradeoff among PSNR, hiding capacity, and computation complexity.