Statistical Steganalysis of Images

Min Huang, Purdue University

Abstract

Steganalysis is the study of detecting secret information hidden in objects such as images, videos, texts, time series and games via steganography. Among those objects, the image is the most widely used object to hide secret messages. Detection of possible secret information hidden in images has attracted a lot of attention over the past ten years. People may conduct covert communications by exchanging images in which secret messages may be embedded in bits. One of main advantages of steganography over cryptography is that the former makes this communication insensible for human beings. So statistical methods or tools are needed to help distinguish cover images from stego images.In this thesis, we start with a discussion of image steganography. Different kinds of embedding schemes for hiding secret information in images are investigated. We also propose a hiding scheme using a reference matrix to lower the distortion caused by embedding. As a result, we obtain Peak Signal-to-Noise Ratios (PSNRs) of stego images that are higher than those given by a Sudoku-based embedding scheme. Next, we consider statistical steganalysis of images in two different frameworks. We first study staganalysis in the framework of statistical hypothesis testing. That is, we cast a cover/stego image detection problem as a hypothesis testing problem. For this purpose, we employ different statistical models for cover images and simulate the effects caused by secret information embedding operations on cover images. Then the staganalysis can be characterized by a hypothesis testing problem in terms of the embedding rate. Rao’s score statistic is used to help make a decision. The main advantage of using Rao’s score test for this problem is that it eliminates an assumption used in the previous work where approximated log likelihood ratio (LR) statistics were commonly employed for the hypothesis testing problems.We also investigate steganalysis using the deep learning framework. Motivated by neural network architectures applied in computer vision and other tasks, we propose a carefully designed a deep convolutional neural network architecture to classify the cover and stego images. We empirically show the proposed neural network outperforms the state-of-the-art ensemble classifier using a rich model, and is also comparable to other convolutional neural network architectures used for steganalysis.

Degree

Ph.D.

Advisors

Rego, Purdue University.

Subject Area

Statistics|Design|Communication|Agronomy|Artificial intelligence|Computer science

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