Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2 or bpp (bits per pixel) of 23.57 by changing only 0.76 of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.
Recently there are some works on applying neural networks for steganography. El-Emam @cite_22 and Saleema @cite_1 work on using neural networks to refine the embedded image generated via traditional steganography methods, i.e., LSB method. Volkhonskiy's @cite_48 and Shi's @cite_34 work focus on generating secure cover images for traditional steganography methods to apply image steganography. Baluja @cite_31 is working on the same field as StegNet. However, the hidden image is slightly visible on residual images of the generated embedded images. Moreover, his architecture uses three networks which requires much more GPU memory and takes more time to embed.
@cite_22 @cite_48 @cite_1 @cite_31 @cite_34
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Abstract In recent years, image steganography has been one of the emerging research areas. As the field of information technology is advancing, the need of information security is increasing day by day. Steganography is a widely used communication method in today's scenario which involves sending secret information in appropriate carriers. Since it have an interesting property of concealing the message as well as the existence of the message, steganography is on its evolutionary path to unearth new platforms. As the field of steganalysis is growing exponentially, the need of developing strong steganographic algorithms is also growing. Since the use of steganography is spreading across various fields, the goal of increasing the embedding capacity, security and image quality is being major concerns. We propose a new image steganographic method which is based on random selection of pixels for secret data embedding and post processing the stego-image using Hybrid Fuzzy Neural Networks. The pixels where secret data is to be embedded is selected randomly using a pseudo random key. In the selected pixels the last 2 or 3 bits are used for hiding. The resultant degradation in the quality of stego-image is handled by an efficient pixel adjustment process with the use of fuzzy neural networks.. The experimental results reveal that this method can achieve an embedding capacity of 3 bits per byte with excellent stego-image quality and high imperceptibility. Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography's ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed. This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools. Steganography is collection of methods to hide secret information ("payload") within non-secret information ("container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications. In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method. All the existing image steganography methods use manually crafted features to hide binary payloads into cover images. This leads to small payload capacity and image distortion. Here we propose a convolutional neural network based encoder-decoder architecture for embedding of images as payload. To this end, we make following three major contributions: (i) we propose a deep learning based generic encoder-decoder architecture for image steganography; (ii) we introduce a new loss function that ensures joint end-to-end training of encoder-decoder networks; (iii) we perform extensive empirical evaluation of proposed architecture on a range of challenging publicly available datasets (MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art payload capacity at high PSNR and SSIM values. Abstract Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image. Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits. In this paper, we construct and implement a new Steganography algorithm based on learning system to hide a large amount of information into color BMP image. We have used adaptive image filtering and adaptive non-uniform image segmentation with bits replacement on the appropriate pixels. These pixels are selected randomly rather than sequentially by using new concept defined by main cases with sub cases for each byte in one pixel. According to the steps of design, we have been concluded 16 main cases with their sub cases that covere all aspects of the input information into color bitmap image. High security layers have been proposed through four layers of security to make it difficult to break the encryption of the input information and confuse steganalysis too. Learning system has been introduces at the fourth layer of security through neural network. This layer is used to increase the difficulties of the statistical attacks. Our results against statistical and visual attacks are discussed before and after using the learning system and we make comparison with the previous Steganography algorithm. We show that our algorithm can embed efficiently a large amount of information that has been reached to 75 of the image size (replace 18 bits for each pixel as a maximum) with high quality of the output. This paper describes a method for checking the acceptability of paraphrases in context. We use the Google n-gram data and a CCG parser to certify the paraphrasing grammaticality and fluency. We collect a corpus of human judgements to evaluate our system. The ultimate goal of our work is to integrate text paraphrasing into a Linguistic Steganography system, by using paraphrases to hide information in a cover text. We propose automatically generated paraphrases as a new and useful source of transformations for Linguistic Steganography, and show that our method for checking paraphrases is effective at maintaining a high level of imperceptibility, which is crucial for effective steganography. Steganography aims to conceal the very fact that the communication takes place, by embedding a message into a digit object such as image without introducing noticeable artifacts. A number of steganographic systems have been developed in past years, most of which, however, are confined to the laboratory conditions where the real-world use of steganography are rarely concerned. In this paper, we introduce an alternative perspective to steganography. A graph-theoretic model to steganography on social networks is presented to analyze real-world steganographic scenarios. In the graph, steganographic participants are corresponding to the vertices with meaningless unique identifiers. Each edge allows the two vertices to communicate with each other by any steganographic algorithm. Meanwhile, the edges are associated with weights to quantize the corresponding communication risk (or say cost). The optimization task is to minimize the overall risk, which is modeled as additive over the social network. We analyze different scenarios on a social network, and provide the suited solutions to the corresponding optimization tasks. We prove that a multiplicative probabilistic graph is equivalent to an additive weighted graph. From the viewpoint of an attacker, he may hope to detect suspicious communication channels, the data encoder(s) and the data decoder(s). We present limited detection analysis to steganographic communication on a network. Watermarking, which belong to the information hiding field, has seen a lot of research interest. There is a lot of work begin conducted in different branches in this field. Steganography is used for secret communication, whereas watermarking is used for content protection, copyright management, content authentication and tamper detection. In this paper we present a detailed survey of existing and newly proposed steganographic and watermarking techniques. We classify the techniques based on different domains in which data is embedded. We limit the survey to images only. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary ±1 embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes. Abstract It is a hot topic in the design of information hiding algorithm by a combination of compressed technique and interpolation technique in the future. Higher capacity and better image quality are one of the foremost research contents of information hiding. In recent years, an adaptive information hiding has been a hot content of research in the field of information hiding and network security. This paper proposed an adaptive image steganography using absolute moment block truncation coding compression (AMBTC compression) and interpolation technique (ASAI), which can improve the performance of information hiding scheme. The ASAI scheme proposed can offer the benefits of high embedding capacity with low computational complexity and good image quality. The experimental results demonstrate that the proposed ASAI scheme has superior performance than others. Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems. This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level. Abstract Until today, high-capacity is still one of important research parts in information hiding. After the secret information embedded is extracted, the demand for the image reversibility for the total recovery of the original object without any distortion goes high. This paper proposes a high capacity steganography using multilayer embedding (CRS), which can enhance the performance of information hiding system. The experimental results show the proposed CRS scheme has better performance than others. Moreover, the proposed CRS method can display the advantages of good quality image and low complexity of computation.