Theoretical model of the FLD ensemble classifier based on hypothesis testing theory

International audienceThe FLD ensemble classifier is a widely used machine learning tool for steganalysis of digital media due to its efficiency when working with high dimensional feature sets. This paper explains how this classifier can be formulated within the framework of optimal detection by using an accurate statistical model of base learners' projections and the hypothesis testing theory. A substantial advantage of this formulation is the ability to theoretically establish the test properties, including the probability of false alarm and the test power, and the flexibility to use other criteria of optimality than the conventional total probability of error. Numerical results on real images show the sharpness of the theoretically established results and the relevance of the proposed methodology

An Iterative Learning Algorithm for Deciphering Stegoscripts: a Grammatical Approach for Motif Discovery

Steganography, or information hiding, is to conceal the existence of messages so as to protect their confidentiality. We consider de-ciphering a stegoscript, a text with secret messages embedded within a covertext, and identifying the vocabularies used in the mes-sages, with no knowledge of the vocabularies and grammar in which the script was writ-ten. Our research was motivated by the prob-lem of identifying conserved non-coding func-tional elements (motifs) in regulatory regions of genome sequences, which we view as stego-scripts constructed by nature with a statis-tical model consisting of a dictionary and a grammar. We develop an iterative learning algorithm, WordSpy, to learn such a model from a stegoscript. The model then can be applied to identify the embedded secret mes-sages, i.e., the functional motifs. Our algo-rithm can successfully recover the most pos-sible text of the first ten chapters of a novel embedded in a stegoscript and identify the transcription factor binding motifs in the up-stream regions of ∼ 800 yeast genes

Information Hiding with Data Diffusion Using Convolutional Encoding for Super-Encryption

The unification of data encryption with information hiding methods continues to receive significant attention because of the importance of protecting encrypted information by making it covert. This is because one of the principal limitations in any cryptographic system is that encrypted data flags the potential importance of the data (i.e. the plaintext information that has been encrypted) possibly leading to the launch of an attack which may or may not be successful. Information hiding overcomes this limitation by making the data (which may be the plaintext or the encrypted plaintext) imperceptible, the security of the hidden information being compromised if and only if its existence is detected. We consider two functions f1(r) and f2(r) for r ∈ R n , n = 1, 2, 3, ... and the problem of ‘Diffusing’ these functions together, applying a process we call ‘Stochastic Diffusion’ to the diffused field and then hiding the output of this process into one of the two functions. The coupling of these two processes using a form of conditioning that generates a well-posed inverse solution yields a super-encrypted field that is dataconsistent. After presenting the basic encryption method and (encrypted) information hiding model coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management. In this context, we also provide prototype software using m-code and Python for readers to use, improve upon and develop further for applications of interest

Hunting wild stego images, a domain adaptation problem in digital image forensics

Digital image forensics is a field encompassing camera identication, forgery detection and steganalysis. Statistical modeling and machine learning have been successfully applied in the academic community of this maturing field. Still, large gaps exist between academic results and applications used by practicing forensic analysts, especially when the target samples are drawn from a different population than the data in a reference database. This thesis contains four published papers aiming at narrowing this gap in three different fields: mobile stego app detection, digital image steganalysis and camera identification. It is the first work to explore a way of extending the academic methods to real world images created by apps. New ideas and methods are developed for target images with very rich flexibility in the embedding rates, embedding algorithms, exposure settings and camera sources. The experimental results proved that the proposed methods work very well, even for the devices which are not included in the reference database

Machine learning based digital image forensics and steganalysis

The security and trustworthiness of digital images have become crucial issues due to the simplicity of malicious processing. Therefore, the research on image steganalysis (determining if a given image has secret information hidden inside) and image forensics (determining the origin and authenticity of a given image and revealing the processing history the image has gone through) has become crucial to the digital society. In this dissertation, the steganalysis and forensics of digital images are treated as pattern classification problems so as to make advanced machine learning (ML) methods applicable. Three topics are covered: (1) architectural design of convolutional neural networks (CNNs) for steganalysis, (2) statistical feature extraction for camera model classification, and (3) real-world tampering detection and localization. For covert communications, steganography is used to embed secret messages into images by altering pixel values slightly. Since advanced steganography alters the pixel values in the image regions that are hard to be detected, the traditional ML-based steganalytic methods heavily relied on sophisticated manual feature design have been pushed to the limit. To overcome this difficulty, in-depth studies are conducted and reported in this dissertation so as to move the success achieved by the CNNs in computer vision to steganalysis. The outcomes achieved and reported in this dissertation are: (1) a proposed CNN architecture incorporating the domain knowledge of steganography and steganalysis, and (2) ensemble methods of the CNNs for steganalysis. The proposed CNN is currently one of the best classifiers against steganography. Camera model classification from images aims at assigning a given image to its source capturing camera model based on the statistics of image pixel values. For this, two types of statistical features are designed to capture the traces left by in-camera image processing algorithms. The first is Markov transition probabilities modeling block-DCT coefficients for JPEG images; the second is based on histograms of local binary patterns obtained in both the spatial and wavelet domains. The designed features serve as the input to train support vector machines, which have the best classification performance at the time the features are proposed. The last part of this dissertation documents the solutions delivered by the author’s team to The First Image Forensics Challenge organized by the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. In the competition, all the fake images involved were doctored by popular image-editing software to simulate the real-world scenario of tampering detection (determine if a given image has been tampered or not) and localization (determine which pixels have been tampered). In Phase-1 of the Challenge, advanced steganalysis features were successfully migrated to tampering detection. In Phase-2 of the Challenge, an efficient copy-move detector equipped with PatchMatch as a fast approximate nearest neighbor searching method were developed to identify duplicated regions within images. With these tools, the author’s team won the runner-up prizes in both the two phases of the Challenge

Natural Image Statistics for Digital Image Forensics

We describe a set of natural image statistics that are built upon two multi-scale image decompositions, the quadrature mirror filter pyramid decomposition and the local angular harmonic decomposition. These image statistics consist of first- and higher-order statistics that capture certain statistical regularities of natural images. We propose to apply these image statistics, together with classification techniques, to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection. We also apply these image statistics to the traditional art authentication for forgery detection and identification of artists in an art work. For each application we show the effectiveness of these image statistics and analyze their sensitivity and robustness

Analytical Review on Graphical Formats Used in Image Steganographic Compression

This paper reviews the method of classification of the types of images used in data concealment based on the perspective of the researcher’s efforts in the past decade.Therefore, all papers were analyzed and classified according to time periods.The main objective of the study is to infer the best types of images that researchers have discussed and used, several reasons will be shown in this study, which started from 2006 to 2017, through this paper the pros and the cons in the use of favourite types in the concealment of data through previous studies