Remi Cogranne

Bio: Remi Cogranne is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Statistical hypothesis testing & Steganalysis. The author has an hindex of 23, co-authored 92 publications receiving 2185 citations. Previous affiliations of Remi Cogranne include University of Technology of Troyes.

Content-Adaptive Steganography by Minimizing Statistical Detectability

Abstract: Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, the state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability limited sender and estimate the secure payload of individual images.

Selection-channel-aware rich model for Steganalysis of digital images

Abstract: From the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic slection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-the-art content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover.

Moving steganography and steganalysis from the laboratory into the real world

Abstract: There has been an explosion of academic literature on steganography and steganalysis in the past two decades. With a few exceptions, such papers address abstractions of the hiding and detection problems, which arguably have become disconnected from the real world. Most published results, including by the authors of this paper, apply "in laboratory conditions" and some are heavily hedged by assumptions and caveats; significant challenges remain unsolved in order to implement good steganography and steganalysis in practice. This position paper sets out some of the important questions which have been left unanswered, as well as highlighting some that have already been addressed successfully, for steganography and steganalysis to be used in the real world.

Rich model for Steganalysis of color images

Abstract: In this paper, we propose an extension of the spatial rich model for steganalysis of color images. The additional features are formed by three- dimensional co-occurrences of residuals computed from all three color channels and their role is to capture dependencies across color channels. These CRMQ1 (color rich model) features are extremely powerful for detection of steganography in images that exhibit traces of color interpolation. Content-adaptive algo- rithms seem to be hurt much more because of their ten- dency to modify the same pixels in each channel. The efficiency of the proposed feature set is demonstrated on three different color versions of BOSSbase 1.01 and two steganographic algorithms – the non-adaptive LSB matching and WOW.

Camera Model Identification Based on the Heteroscedastic Noise Model

Abstract: The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performances are theoretically established. For a practical use, two generalized LRTs are designed to deal with unknown model parameters so that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural raw images highlight the relevance of the proposed approach.

I and i

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 …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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