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Denis Volkhonskiy

Bio: Denis Volkhonskiy is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Steganalysis & Steganography. The author has an hindex of 7, co-authored 14 publications receiving 258 citations.

Papers
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Proceedings ArticleDOI
31 Jan 2020
TL;DR: In this article, a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN) is proposed, which allows to generate more setganalysis-secure message embedding using standard steganography algorithms.
Abstract: 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.

101 citations

Posted Content
TL;DR: A new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN) is proposed, which allows to generate more setganalysis-secure message embedding using standard steganography algorithms.
Abstract: 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.

100 citations

Posted Content
TL;DR: This work addresses the problem of high computational requirements of generative models for videos via modeling the dynamics in a latent space after the transformation of frames into the latent space, and predicts latent representation for the next frames in an autoregressive manner.
Abstract: The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computational requirements. Some models require up to 512 Tensor Processing Units for parallel training. In this work, we address this problem via modeling the dynamics in a latent space. After the transformation of frames into the latent space, our model predicts latent representation for the next frames in an autoregressive manner. We demonstrate the performance of our approach on BAIR Robot Pushing and Kinetics-600 datasets. The approach tends to reduce requirements to 8 Graphical Processing Units for training the models while maintaining comparable generation quality.

66 citations

24 Apr 2017
TL;DR: A new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN) is proposed, which allows to generate more setganalysis-secure message embedding using standard steganography algorithms.
Abstract: 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.

28 citations

Posted Content
TL;DR: This work proposes a new method for change-point detection based on Inductive Conformal Martingales, which requires only the independence and identical distribution of observations, and compares the proposed approach to standard methods, as well as to change- point detection oracles.
Abstract: We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we propose a new method for change-point detection based on Inductive Conformal Martingales, which requires only the independence and identical distribution of observations. We compare the proposed approach to standard methods, as well as to change-point detection oracles, which model a typical practical situation when we have only imprecise (albeit parametric) information about pre- and post-change data distributions. Results of comparison provide evidence that change-point detection based on Inductive Conformal Martingales is an efficient tool, capable to work under quite general conditions unlike traditional approaches.

26 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Posted Content
TL;DR: This paper attempts to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications, and compares the commonalities and differences of these GAns methods.
Abstract: Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

344 citations

Journal ArticleDOI
TL;DR: A novel image SWE method based on deep convolutional generative adversarial networks that has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.
Abstract: The security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.

180 citations

Journal ArticleDOI
TL;DR: In this paper, the authors discuss the details of GANs for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives.
Abstract: Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.

177 citations

Posted Content
TL;DR: An intriguing property of CycleGAN is demonstrated: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal, to satisfy the cyclic consistency requirement, while the generated image remains realistic.
Abstract: CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic. We connect this phenomenon with adversarial attacks by viewing CycleGAN's training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks.

161 citations