Double-Compressed JPEG Detection in a Steganalysis System

The detection of hidden messages in JPEG images is a growing concern. Current detection of JPEG stego images must include detection of double compression: a JPEG image is double compressed if it has been compressed with one quality factor, uncompressed, and then re-compressed with a different quality factor. When detection of double compression is not included, erroneous detection rates are very high. The main contribution of this paper is to present an efficient double-compression detection algorithm that has relatively lower dimensionality of features and relatively lower computational time for the detection part, than current comparative classifiers. We use a model-based approach for creating features, using a subclass of Markov random fields called partially ordered Markov models (POMMs) to modeling the phenomenon of the bit changes that occur in an image after an application of steganography. We model as noise the embedding process, and create features to capture this noise characteristic. We show that the nonparametric conditional probabilities that are modeled using a POMM can work very well to distinguish between an image that has been double compressed and one that has not, with lower overall computational cost. After double compression detection, we analyze histogram patterns that identify the primary quality compression factor to classify the image as stego or cover. The latter is an analytic approach that requires no classifier training. We compare our results with another state-of-the-art double compression detector. Keywords: steganalysis; steganography; JPEG; double compression; digital image forensics

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Steganalytic Methods for 3D Objects

This PhD thesis provides new research results in the area of using 3D features for steganalysis. The research study presented in the thesis proposes new sets of 3D features, greatly extending the previously proposed features. The proposed steganlytic feature set includes features representing the vertex normal, curvature ratio, Gaussian curvature, the edge and vertex position of the 3D objects in the spherical coordinate system. Through a second contribution, this thesis presents a 3D wavelet multiresolution analysis-based steganalytic method. The proposed method extracts the 3D steganalytic features from meshes of different resolutions. The third contribution proposes a robustness and relevance-based feature selection method for solving the cover-source mismatch problem in 3D steganalysis. This method selects those 3D features that are robust to the variation of the cover source, while preserving the relevance of such features to the class label. All the proposed methods are applied for identifying stego-meshes produced by several steganographic algorithms

Using multiple re-embeddings for quantitative steganalysis and image reliability estimation

The quantitative steganalysis problem aims at estimating the amount of payload embedded inside a document. In this paper, JPEG images are considered, and by the use of a re-embedding based methodology, it is possible to estimate the number of original embedding changes performed on the image by a stego source and to slightly improve the estimation regarding classical quantitative steganalysis methods. The major advance of this methodology is that it also enables to obtain a confidence interval on this estimated payload. This confidence interval then permits to evaluate the difficulty of an image, in terms of steganalysis by estimating the reliability of the output. The regression technique comes from the OP-ELM and the reliability is estimated using linear approximation. The methodology is applied with a publicly available stego algorithm, regression model and database of images. The methodology is generic and can be used for any quantitative steganalysis problem of this class

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