Showing papers on "Steganography published in 2019"

Deep Residual Network for Steganalysis of Digital Images

Abstract: Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.

CNN-Based Adversarial Embedding for Image Steganography

Abstract: Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artifacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding (ADV-EMB), which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN)-based steganalyzer. The proposed method works under the conventional framework of distortion minimization. In particular, ADV-EMB adjusts the costs of image elements modifications according to the gradients back propagated from the target CNN steganalyzer. Therefore, modification direction has a higher probability to be the same as the inverse sign of the gradient. In this way, the so-called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme achieves better security performance against the target adversary-unaware steganalyzer by increasing its missed detection rate. In addition, it deteriorates the performance of other adversary-aware steganalyzers, opening the way to a new class of modern steganographic schemes capable of overcoming powerful CNN-based steganalysis.

Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction

Abstract: Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set $\alpha$ -positive region reduction. First, it is pointed out that decision rough set $\alpha$ -positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set $\alpha$ -positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection.

RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks

Abstract: Linguistic steganography based on text carrier auto-generation technology is a current topic with great promise and challenges. Limited by the text automatic generation technology or the corresponding text coding methods, the quality of the steganographic text generated by previous methods is inferior, which makes its imperceptibility unsatisfactory. In this paper, we propose a linguistic steganography based on recurrent neural networks, which can automatically generate high-quality text covers on the basis of a secret bitstream that needs to be hidden. We trained our model with a large number of artificially generated samples and obtained a good estimate of the statistical language model. In the text generation process, we propose fixed-length coding and variable-length coding to encode words based on their conditional probability distribution. We designed several experiments to test the proposed model from the perspectives of information hiding efficiency, information imperceptibility, and information hidden capacity. The experimental results show that the proposed model outperforms all the previous related methods and achieves the state-of-the-art performance.

An Algorithm for Security Enhancement in Image Transmission Using Steganography

Abstract: Data communication through the public communication channels is insecure due to advanced technology available for interception of third party users. Therefore, an efficient data hiding technology is vital for secure data transmission especially when the system is connected with internet services. Audio steganography is one of the promising solutions for where the information is hidden over audio file. In this paper, a novel method for the hiding of image information by converting into another format thereby reduces the computational complexity.

Towards Robust Image Steganography

Abstract: Posting images on social network platforms is happening everywhere and every single second. Thus, the communication channels offered by various social networks have a great potential for covert communication. However, images transmitted through such channels will usually be JPEG compressed, which fails most of the existing steganographic schemes. In this paper, we propose a novel image steganography framework that is robust for such channels. In particular, we first obtain the channel compressed version (i.e., the channel output) of the original image. Secret data is embedded into the channel compressed original image by using any of the existing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image before the channel transmission (termed the intermediate image), we propose a coefficient adjustment scheme to slightly modify the original image based on the stego-image. The adjustment is done such that the channel compressed version of the intermediate image is exactly the same as the stego-image. Therefore, after the channel transmission, secret data can be extracted from the stego-image with 100% accuracy. Various experiments are conducted to show the effectiveness of the proposed framework for image steganography robust to JPEG compression.

A novel image steganography technique based on quantum substitution boxes

Abstract: Substitution boxes play an essential role in designing secure cryptosystems. With the evolution of quantum technologies, current data security mechanisms may be broken due to their construction based on mathematical computation. Quantum walks, a universal quantum computational model, play an essential role in designing quantum algorithms. We utilize the benefits of quantum walks to present a novel technique for constructing substitution boxes (S-boxes) based on quantum walks (QWs). The performance of the presented QWs S-box technique is evaluated by S-box evaluation criteria, and our results prove that the constructed S-box has vital qualities for viable applications in security purposes. Furthermore, a new technique for image steganography is constructed. The proposed technique is an integrated mechanism between classical data hiding and quantum walks to achieve better security for the embedded data. The embedding and extraction procedures are controlled by QWs S-box. The inclusion of cryptographic QWs S-box ensures the security of both embedding and extraction phases. At the extraction phase, only the stego image and the secret values are needed for constructing the secret data. Experimental results demonstrate that the presented technique has a high-security, high embedding capacity and good visual quality.

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Abstract: Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this paper, we create covert and scattered triggers for backdoor attacks, invisible backdoors, where triggers can fool both DNN models and human inspection. We apply our invisible backdoors through two state-of-the-art methods of embedding triggers for backdoor attacks. The first approach on Badnets embeds the trigger into DNNs through steganography. The second approach of a trojan attack uses two types of additional regularization terms to generate the triggers with irregular shape and size. We use the Attack Success Rate and Functionality to measure the performance of our attacks. We introduce two novel definitions of invisibility for human perception; one is conceptualized by the Perceptual Adversarial Similarity Score (PASS) and the other is Learned Perceptual Image Patch Similarity (LPIPS). We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as four datasets MNIST, CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates for the adversary, functionality for the normal users, and invisibility scores for the administrators. We finally argue that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches, such as Neural Cleanse and TABOR.

Review on optical image hiding and watermarking techniques

Abstract: Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access. Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared to digital approaches. This paper provides a comprehensive review of the research works related to optical image hiding and watermarking techniques conducted in the past decade. The past research works have focused on two major aspects: various optical systems for image hiding, and the methods for embedding the optical system output into a host image. A summary of the state-of-the-art works is made from these two perspectives.

Coverless image steganography using partial-duplicate image retrieval

Abstract: Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. In this paper, we propose a novel coverless steganographic approach without any modification for transmitting secret color image. In our approach, instead of modifying a cover image to generate the stego-image, steganography is realized by using a set of proper partial duplicates of a given secret image as stego-images, which are retrieved from a natural image database. More specifically, after dividing each database image into a number of non-overlapping patches and indexing those images based on the features extracted from these patches, we search for the partial duplicates of the secret image in the database to obtain the stego-images, each of which shares one or several visually similar patches with the secret image. At the receiver end, by using the patches of the stego-images, our approach can approximately recover the secret image. Since the stego-images are natural ones without any modification traces, our approach can resist all of the existing steganalysis tools. Experimental results and analysis prove that our approach not only has strong resistance to steganalysis, but also has desirable security and high hiding capability.

Hiding Data in Images Using Cryptography and Deep Neural Network

Abstract: Steganography is an art of obscuring data inside another quotidian file of similar or varying types. Hiding data has always been of significant importance to digital forensics. Previously, steganography has been combined with cryptography and neural networks separately. Whereas, this research combines steganography, cryptography with the neural networks all together to hide an image inside another container image of the larger or same size. Although the cryptographic technique used is quite simple, but is effective when convoluted with deep neural nets. Other steganography techniques involve hiding data efficiently, but in a uniform pattern which makes it less secure. This method targets both the challenges and make data hiding secure and non-uniform.

Invisible steganography via generative adversarial networks

Abstract: Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, PASCAL-VOC12 and ImageNet datasets.

SteganoGAN: High Capacity Image Steganography with GANs.

Abstract: Image steganography is a procedure for hiding messages inside pictures. While other techniques such as cryptography aim to prevent adversaries from reading the secret message, steganography aims to hide the presence of the message itself. In this paper, we propose a novel technique for hiding arbitrary binary data in images using generative adversarial networks which allow us to optimize the perceptual quality of the images produced by our model. We show that our approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades detection by steganalysis tools, and is effective on images from multiple datasets. To enable fair comparisons, we have released an open source library that is available online at this https URL.

Reversible Image Steganography Scheme Based on a U-Net Structure

Abstract: Traditional steganography methods often hide secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this paper, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and sends it to the receiver. Finally, the receiver uses the extraction network to reconstruct the secret image and original cover image correctly. The experimental results show that the proposed scheme compresses and distributes the information of the embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.

Light Field Messaging With Deep Photographic Steganography

Abstract: We develop Light Field Messaging (LFM), a process of embedding, transmitting, and receiving hidden information in video that is displayed on a screen and captured by a handheld camera. The goal of the system is to minimize perceived visual artifacts of the message embedding, while simultaneously maximizing the accuracy of message recovery on the camera side. LFM requires photographic steganography for embedding messages that can be displayed and camera-captured. Unlike digital steganography, the embedding requirements are significantly more challenging due to the combined effect of the screen's radiometric emittance function, the camera's sensitivity function, and the camera-display relative geometry. We devise and train a network to jointly learn a deep embedding and recovery algorithm that requires no multi-frame synchronization. A key novel component is the camera display transfer function (CDTF) to model the camera-display pipeline. To learn this CDTF we introduce a dataset (Camera-Display 1M) of 1,000,000 camera-captured images collected from 25 camera-display pairs. The result of this work is a high-performance real-time LFM system using consumer-grade displays and smartphone cameras.

Hiding Data in Images Using Cryptography and Deep Neural Network

Abstract: Steganography is an art of obscuring data inside another quotidian file of similar or varying types. Hiding data has always been of significant importance to digital forensics. Previously, steganography has been combined with cryptography and neural networks separately. Whereas, this research combines steganography, cryptography with the neural networks all together to hide an image inside another container image of the larger or same size. Although the cryptographic technique used is quite simple, but is effective when convoluted with deep neural nets. Other steganography techniques involve hiding data efficiently, but in a uniform pattern which makes it less secure. This method targets both the challenges and make data hiding secure and non-uniform.

The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis

Abstract: This paper presents ins and outs of the ALASKA challenge, a steganalysis challenge built to reflect the constraints of a forensic steganalyst. We motivate and explain the main differences w.r.t. the BOSS challenge (2010), specifically the use of a ranking metric prescribing high false positive rates, the analysis of a large diversity of different image sources and the use of a collection of steganographic schemes adapted to handle color JPEGs. The core of the challenge is also described, this includes the RAW image data-set, the implementations used to generate cover images and the specificities of the embedding schemes. The very first outcomes of the challenge are then presented, and the impacts of different parameters such as demosaicking, filtering, image size, JPEG quality factors and cover-source mismatch are analyzed. Eventually, conclusions are presented, highlighting positive and negative points together with future directions for the next challenges in practical steganalysis.

An integer wavelet transform image steganography method based on 3D sine chaotic map

Abstract: Steganography is one of the well-known data hiding methods, which is used in many security companies and government communications. In this technique, the various types of digital media can be used as a cover to hide secret information without impress the general form of them. Generally, key space and security are two important matters in most steganography methods, and they have the direct impact in proposed method’s security. Moreover, Chaotic maps have been used in many data hiding algorithms to increase the security and key space of proposed schemes. The main reasons of using chaotic maps in steganography methods are sensitivity to initial conditions and control parameters. This paper proposes a new steganography technique based on new 3d sine chaotic map. This map is used in embedding and extracting processes to increase the security of the proposed algorithm. Satisfactory performance, acceptable image distortion and stronger robustness against some attacks are the main features of proposed method, and they are shown in experimental results. Comparison with some existing method shows that the quality and performance of the proposed algorithm are good, and it has high security and acceptable robustness against cropping and salt & pepper attacks.

Feature selection for image steganalysis using levy flight-based grey wolf optimization

Abstract: Image steganalysis is the process of detecting the availability of hidden messages in the cover images. Therefore, it may be considered as a classification problem which categorizes an image either into a cover images or a stego image. Feature selection is one of the important phases of image steganalysis which can increase its computational efficiency and performance. In this paper, a novel levy flight-based grey wolf optimization has been introduced which is used to select the prominent features for steganalysis algorithm from a set of original features. For the same, SPAM and AlexNet have been used to generate the high dimensional features. Furthermore, the random forest classifier is used to classify the images over selected features into cover images and stego images. The experimental results show that the proposed levy flight-based grey wolf optimization shows preferable convergence precision and effectively reduces the irrelevant and redundant features while maintaining the high classification accuracy as compared to other feature selection methods.

Very High Capacity Image Steganography Technique Using Quotient Value Differencing and LSB Substitution

Abstract: This article proposes a very high capacity steganography technique using differencing and substitution mechanisms. It divides the image into non-overlapped $$3{\times }3$$ pixel blocks. For every pixel of a block, least significant bit (LSB) substitution is applied on two LSBs and quotient value differencing (QVD) is applied on the remaining six bits. Thus, there are two levels of embedding: (i) LSB substitution at lower bit planes and (ii) QVD at higher bit planes. If a block after embedding in this fashion suffers with fall off boundary problem, then that block is undone from the above hybrid embedding and modified 4-bit LSB substitution is applied. Experimentally, it is evidenced that the hiding capacity is improved to a greater extent. It is also experimentally proved that pixel difference histogram and RS analysis techniques cannot detect the proposed steganography technique.

Improving embedding efficiency for digital steganography by exploiting similarities between secret and cover images

Abstract: Digital steganography is becoming a common tool for protecting sensitive communications in various applications such as crime/terrorism prevention whereby law enforcing personals need to remotely compare facial images captured at the scene of crime with faces databases of known criminals/suspects; exchanging military maps or surveillance video in hostile environment/situations; privacy preserving in the healthcare systems when storing or exchanging patient’s medical images/records; and prevent bank customers’ accounts/records from being accessed illegally by unauthorized users. Existing digital steganography schemes for embedding secret images in cover image files tend not to exploit various redundancies in the secret image bit-stream to deal with the various conflicting requirements on embedding capacity, stego-image quality, and undetectibility. This paper is concerned with the development of innovative image procedures and data hiding schemes that exploit, as well as increase, similarities between secret image bit-stream and the cover image LSB plane. This will be achieved in two novel steps involving manipulating both the secret and the cover images, prior to embedding, to achieve higher 0:1 ratio in both the secret image bit-stream and the cover image LSB plane. The above two steps strategy has been exploited to use a bit-plane(s) mapping technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This paper will demonstrate that this strategy produces stego-images that have minimal distortion, high embedding efficiency, reasonably good stego-image quality and robustness against 3 well-known targeted steganalysis tools.

Improving the Robustness of Adaptive Steganographic Algorithms Based on Transport Channel Matching

Abstract: Moving steganography and steganalysis from the laboratory into the real world, the robustness of steganography needs to be further considered. In this paper, we propose a robust steganographic algorithm to resist the JPEG compression of transport channel based on transport channel matching. Transport channel matching can adjust images to meet the requirements of transport channel so that the impact of JPEG compression from the channel can be reduced. To improve the robustness of steganography, the embedded message bits will be encoded by the error correction code. Then, the adaptive steganographic algorithms will be used to embed messages. To enhance the coding rate, the error correction capability ${t}$ of the error correction code is dynamically adjusted according to the images. Experimental results on the local simulation of JPEG compression and social network site demonstrate that the proposed steganographic algorithm has a good performance with respect to both robustness and security.

A Fast and Efficient Text Steganalysis Method

Abstract: With the rapid development of natural language processing technology in the past few years, the steganography by text synthesis has been greatly developed. These methods can analyze the statistical feature distribution of a large number of training samples, and then generate steganographic texts that conform to such statistical distribution. For these steganography methods, previous steganalysis methods show unsatisfactory detection performance, which remains an unsolved problem and poses a great threat to the security of cyberspace. In this letter, we proposed a fast and efficient text steganalysis method to solve this problem. We first analyzed the correlations between words in these generated steganographic texts. Then, we map each word to a semantic space and used a hidden layer to extract the correlations between these words. Finally, based on the extracted correlation features, we used the softmax classifier to classify the input text. Experimental results show that the proposed model can achieve a high detection accuracy, which shows a state-of-the-art performance.

Image Based Steganography to Facilitate Improving Counting-Based Secret Sharing

Abstract: The secret sharing scheme is a security tool that provides reliability and robustness for multi-user authentication systems. This work focuses on improving the security and efficiency of counting-based secret sharing by remodeling its phases. The research presents refining the shares generation phase as well as proposing different secret reconstruction models based on new shares distribution method. This work addressed solving published defects in the original counting-based secret sharing scheme providing optimized efficient methodology. The research further proposed utilizing steganography for practicality purposes. We adopted image-based steganography to hide generated shares preserving the improved security of the scheme enhancing the humanized remembrance usability. The study compared several applicable steganography methods for hiding shares analyzing their distortion, security, and capacity. This facilitating work of image-based steganography with the improved counting-based secret sharing is showing promising results opening research direction for future attractive contributions to follow-on.

A secure steganography creation algorithm for multiple file formats

Abstract: The technological advancements in the information sharing and the development of many techniques to make the information conveyance easy necessitate a protection methodology that could prevent the personal information that is transmitted from being hacked. Some of the protection methods that have emerged are the cryptography, steganography, watermarking, digital signature etc. As steganography is a popular method of transmitting the covered secret information in order to avoid hacking and the misuse due to its major attributes such as the security, capacity and the robustness. This paper puts forward cryptography incorporated steganography (CICS) to provide a highly secure steganography algorithm for the files of the multiple formats, The performance analysis of the method and the measurement of the parameters such as the PSNR and the SSIM are done to ensure the efficiency of the proffered method.