<title>Steganalysis of block-structured stegotext</title>

Communications in Computer and Information Science, 2009

In recent years, several lossless data hiding techniques have been proposed for images. Lossless data embedding can take place in the spatial domain or in the transform domain. They utilized characteristics of the difference image or the transform coefficient histogram and modify these values slightly to embed the data. However, after embedding message bits these steganography changed the nature of the difference image histogram or the transform coefficient histogram gradually. In this paper, we introduce two new steganalytic techniques based on the difference image histogram and the transform coefficient histogram. The algorithm can not only detect existence of secret messages in images which are embedded by above methods reliably, but also estimate the amount of hidden messages exactly.

Multimedia Systems, 2003

The objective of steganalysis is to detect messages hidden in cover objects, such as digital images. In practice, the steganalyst is frequently interested in more than whether or not a secret message is present. The ultimate goal is to extract and decipher the secret message. However, in the absence of the knowledge of the stego technique and the stego and cipher keys, this task may be extremely time consuming or completely infeasible. Therefore, any additional information, such as the message length or its approximate placement in image features, could prove very valuable to the analyst. In this paper, we present general principles for developing steganalytic methods that can accurately estimate the number of changes to the cover image imposed during embedding. Using those principles, we show how to estimate the secret message length for the most common embedding archetypes, including the F5 and OutGuess algorithms for JPEG, EzStego algorithm with random straddling for palette images, and the classical LSB embedding with random straddling for uncompressed image formats. The paper concludes with an outline of ideas for future research such as estimating the steganographic capacity of embedding algorithms.

Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 2008

This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the cover-and the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas message embedding using QIM increases irregularity in the resulting QIM-stego image. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test-image. Simulation results presented in this paper show that the proposed steganalysis technique can distinguish between the cover-and the stego-image with low false rates (i.e. P f p < 0.1 & P f n < 0.07 for dither modulation stego and P f p < 0.12 & P f n < 0.002 for QIM-stego).

Information Technology Conference, …, 1998

The goal of steganography is to avoid drawing suspicion to the transmission of a hidden message. If suspicion is raised, then this goal is defeated. Discovering and rendering useless such covert messages is a new art form known as steganalysis. In this paper, we provide an overview of some characteristics in information hiding methods that direct the steganalyst to the existence of a hidden message and identify where to look for hidden information.

IET Signal Processing, 2011

This study introduces a new scheme for steganalysis of the least significant bit (LSB) embedding, based on the idea of closure of sets (CoS), which is independent of the type of cover signal, applicable to both spatial and transform domains. The CoS is referred to as some special subsets that could be found in a common space whose elements relate to higher-order statistical properties of the signal. The proposed scheme is used for steganalysis of the LSB steganography of greyscale TIFF and JPEG images and audio signals, employing a set of accurate and monotone features that are extracted based on the CoS definition. It is shown that significant improvement to the detection accuracy in steganalysis is achieved using the proposed scheme, particularly at low embedding rates.

Statistical Steganalysis 1 CHAPTER 1 1.

2003

The process of information hiding is modeled in the context of additive noise. Under an independence assumption, the histogram of the stegomessage is a convolution of the noise probability mass function (PMF) and the original histogram. In the frequency domain this convolution is viewed as a multiplication of the histogram characteristic function (HCF) and the noise characteristic function. Least significant bit, spread spectrum, and DCT hiding methods for images are analyzed in this framework. It is shown that these embedding methods are equivalent to a lowpass filtering of histograms that is quantified by a decrease in the HCF center of mass (COM). These decreases are exploited in a known scheme detection to classify unaltered and spread spectrum images using a bivariate classifier. Finally, a blind detection scheme is built that uses only statistics from unaltered images. By calculating the Mahalanobis distance from a test COM to the training distribution, a threshold is used to identify steganographic images. At an embedding rate of 1 b.p.p. greater than 95% of the stegoimages are detected with false alarm rate of 5%.

Handbook of Statistics, 2005

The goal of steganography is to insert a message into a carrier signal so that it cannot be detected by unintended recipients. Due to their widespread use and availability of bits that can be changed without perceptible damage of the original signal images, video, and audio are widespread carrier media. Steganalysis attempts to discover hidden signals in suspected carriers or at the least detect which media contain hidden signals. Therefore, an important consideration in steganography is how robust to detection is a particular technique. We review the existing steganography and steganalysis techniques and discuss their limitations and some possible research directions.

2007 IEEE International Symposium on Information Theory, 2007

We propose a simple universal (that is, distributionfree) steganographic system in which covertexts with and without hidden texts are statistically indistinguishable. The stegosystem can be applied to any source generating i.i.d. covertexts with unknown distribution, and the hidden text is transmitted exactly, with zero probability of error. Sequences of covertexts with and without hidden information obey the same distribution (the stegosystem is perfectly secure). The proposed steganographic system has two important properties. First, the rate of transmission of hidden information approaches the Shannon entropy of the covertext source as the size of blocks used for hidden text encoding tends to infinity. Second, if the size of the alphabet of the covertext source and its minentropy tend to infinity then the number of bits of hidden text per letter of covertext tends to log(n!)/n where n is the (fixed) size of blocks used for hidden text encoding. Besides, the resource complexity of the proposed algorithms grows only polynomially.

Security, Steganography, and Watermarking of Multimedia Contents VII, 2005

In this paper we study steganalysis, the detection of hidden data. Specifically we focus on detecting data hidden in grayscale images with spread spectrum hiding. To accomplish this we use a statistical model of images and estimate the detectability of a few basic spread spectrum methods. To verify the results of these findings, we create a tool to discriminate between natural "cover" images and "stego" images (containing hidden data) taken from a diverse database. Existing steganalysis schemes that exploit the spatial memory found in natural images are particularly effective. Motivated by this, we include inter-pixel dependencies in our model of image pixel probabilities and use an appropriate statistical measure for the security of a steganography system subject to optimal hypothesis testing. Using this analysis as a guide, we design a tool for detecting hiding on various spread spectrum methods. Depending on the method and power of the hidden message, we correctly detect the presences of hidden data in about 95% of images.