Вероятностные модели наблюдений в стеганографии

Секция 1. Защита информации и компьютерный анализ данныхВ данной статье исследуется стеганографическая система, учитывающая марковскую зависимость. Устанавливается s-мерное распределение вероятностей стегоконтейнера

Об одной задаче вероятностно-статистического анализа вкраплений в двоичную цепь Маркова

In this paper (q, r)-block mathematical model of embeddings in binary Markov chain that appear in steganography is considered. = В статье рассматривается (q, r)-блочная модель вкраплений в двоичную цепь Маркова, возникающая в задачах стеганографической защиты информации. Построены статистические оценки параметров модели на основе частотных статистик. Представлены результаты компьютерных экспериментов

Периодические E-центральные m-кольца

In this article the theorem about a decomposition of a associative ring with identity and commuting idempotents in a subdirect product of A-rings (possessing exactly two idempotents – the null and the identity) is generalized to m-rings. In the theory of m-rings the notion of A-ring corresponds the notion of Е-primary m-ring.The article deals with periodic Е-centra m-rings, i. e. such m-rings (K, +, •, ○), in which every monogenic subsemigroup of the semigroup (K, ○) is fi nite and all idempotents are central. The class of all periodic Е-central m-rings contains all nil-m-rings and all Е-primary periodic m-rings. The main theorem confi rms, that any periodic Е-central m-ring K is either a nil-m-ring or an extention of a nil-m-ring by a subdirect product of Е-primary periodic m-rings. With the set of idempotents of K to be fi nite, the m-ring K is a direct sum of fi nite number of ideals, every of that is a Е-primary periodic m-ring. In turn every Е-primary periodic m-ring is an extention of a nil-m-ring by a division periodic ring. It is shown using the examples, that the condition of periodicity and the condition of Е-centrality in the main theorem can not weakened. = В данной работе обобщается на m-кольца теорема о разложении (ассоциативного) кольца с единицей и коммутирующими идемпотентами в подпрямое произведение А-колец (имеющих только два идемпотента – единицуи нуль) [11, предложение 2]. В теории m-колец имеется аналог этого понятия – Е-примарные m-кольца – это такие m-кольца, у которых ο-почтикольцо имеет в точности два идемпотента – нулевой и единичный элементы. m-Кольцо (К, +, •, ○) называется периодическим, если ο-полугруппа (K, ○) периодична, т. е. всякая ее подполугруппа, порожденная одним элементом, конечна; и m-кольцо называется Е-центральным, если любой его идемпотент перестановочен с любым элементом этого m-кольца. Класс Е-центральных включает в себя все ниль-m-кольца, все Е-примарные и все m-кольца с делением. Основная теорема утверждает, что каждое Е-центральное периодическое m-кольцо К является либо ниль-m-кольцом, либо разлагается в подпрямое произведение некоторого семейства Е-примарных периодических m-колец, либо является расширением ниль-m-кольца при помощи подпрямого произведения некоторого семейства Е-примарных периодических m-колец. При этом если К имеет единицу и конечное число идемпотентов, то К разлагается в прямую сумму конечного числа идеалов, являющихся Е-примарными периодическими m-кольцами. Что касается Е-примарных m-колец, то получена теорема о том, что всякое Е-примарное периодическое m-кольцо есть расширение ниль-m-кольца при помощи периодического m-кольца с делением. Примеры показывают, то условие периодичности и условие Е-центральности в основной теореме нельзя ослабить

Steganography techniques

As cryptography is the standard way of ensuring privacy, integrity and confidentiality of a public channel, steganography steps in to provide even stronger assumptions. Thus, in the case of cryptology, an attacker cannot obtain information about the payload while inspecting its encrypted content. In the case of steganography, one cannot prove the existence of the covert communication itself. The main purpose of the current paper is to provide insights into some of the existing techniques in steganography. A comparison between the performances of several steganography algorithms is accomplished, with focus on the metrics that characterize a steganography technique

Detecting CNN-Generated Facial Images in Real-World Scenarios

Artificial, CNN-generated images are now of such high quality that humans have trouble distinguishing them from real images. Several algorithmic detection methods have been proposed, but these appear to generalize poorly to data from unknown sources, making them infeasible for real-world scenarios. In this work, we present a framework for evaluating detection methods under real-world conditions, consisting of cross-model, cross-data, and post-processing evaluation, and we evaluate state-of-the-art detection methods using the proposed framework. Furthermore, we examine the usefulness of commonly used image pre-processing methods. Lastly, we evaluate human performance on detecting CNN-generated images, along with factors that influence this performance, by conducting an online survey. Our results suggest that CNN-based detection methods are not yet robust enough to be used in real-world scenarios.Comment: Accepted to the workshop on Media Forensics at CVPR 202

Review of steganalysis of digital images

Steganography is the science and art of embedding hidden messages into cover multimedia such as text, image, audio and video. Steganalysis is the counterpart of steganography, which wants to identify if there is data hidden inside a digital medium. In this study, some specific steganographic schemes such as HUGO and LSB are studied and the steganalytic schemes developed to steganalyze the hidden message are studied. Furthermore, some new approaches such as deep learning and game theory, which have seldom been utilized in steganalysis before, are studied. In the rest of thesis study some steganalytic schemes using textural features including the LDP and LTP have been implemented

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)