A Learning-Based Steganalytic Method against LSB Matching Steganography

This paper considers the detection of spatial domain least significant bit (LSB) matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature

Randomized Symmetric Crypto Spatial Fusion Steganographic System

The image fusion steganographic system embeds encrypted messages in decomposed multimedia carriers using a pseudorandom generator but it fails to evaluate the contents of the cover image. This results in the secret data being embedded in smooth regions, which leads to visible distortion that affects the imperceptibility and confidentiality. To solve this issue, as well as to improve the quality and robustness of the system, the Randomized Symmetric Crypto Spatial Fusion Steganography System is proposed in this study. It comprises three-subsystem bitwise encryption, spatial fusion, and bitwise embedding. First, bitwise encryption encrypts the message using bitwise operation to improve the confidentiality. Then, spatial fusion decomposes and evaluates the region of embedding on the basis of sharp intensity and capacity. This restricts the visibility of distortion and provides a high embedding capacity. Finally, the bitwise embedding system embeds the encrypted message through differencing the pixels in the region by 1, checking even or odd options and not equal to zero constraints. This reduces the modification rate to avoid distortion. The proposed heuristic algorithm is implemented in the blue channel, to which the human visual system is less sensitive. It was tested using standard IST natural images with steganalysis algorithms and resulted in better quality, imperceptibility, embedding capacity and invulnerability to various attacks compared to other steganographic systems

A FPGA based Steganographic System Implementing a Modern Steganalysis Resistant LSB Algorithm

Steganography differs from other data hiding techniques because it encodes secret message inside cover object in such a way that transmission of secret message also remains a secret. Widespread usage of digital images, lower computational complexity and better performance makes spatial domain steganographic algorithms well suited for hardware implementation, which are not very frequent. This work tries to implement a modern steganalysis resistant LSB algorithm on FPGA based hardware. The presented work also optimises various operations and elements from original one third probability algorithm with respect to hardware implementation. The target FPGA for the implementation is Xilinx SP605 board (Spartan 6 series XC6SLX45T FPGA). Stego images obtained by the implementation have been thoroughly examined for various qualitative and quantitative aspects, which are found to be at par with original algorithm

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

An analysis of steganographic systems subject to the following perfect undetectability condition is presented in this paper. Following embedding of the message into the covertext, the resulting stegotext is required to have exactly the same probability distribution as the covertext. Then no statistical test can reliably detect the presence of the hidden message. We refer to such steganographic schemes as perfectly secure. A few such schemes have been proposed in recent literature, but they have vanishing rate. We prove that communication performance can potentially be vastly improved; specifically, our basic setup assumes independently and identically distributed (i.i.d.) covertext, and we construct perfectly secure steganographic codes from public watermarking codes using binning methods and randomized permutations of the code. The permutation is a secret key shared between encoder and decoder. We derive (positive) capacity and random-coding exponents for perfectly-secure steganographic systems. The error exponents provide estimates of the code length required to achieve a target low error probability. We address the potential loss in communication performance due to the perfect-security requirement. This loss is the same as the loss obtained under a weaker order-1 steganographic requirement that would just require matching of first-order marginals of the covertext and stegotext distributions. Furthermore, no loss occurs if the covertext distribution is uniform and the distortion metric is cyclically symmetric; steganographic capacity is then achieved by randomized linear codes. Our framework may also be useful for developing computationally secure steganographic systems that have near-optimal communication performance.Comment: To appear in IEEE Trans. on Information Theory, June 2008; ignore Version 2 as the file was corrupte

Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions

An analysis of steganographic systems subject to the following perfect undetectability condition is presented in this paper. Following embedding of the message into the covertext, the resulting stegotext is required to have exactly the same probability distribution as the covertext. Then no statistical test can reliably detect the presence of the hidden message. We refer to such steganographic schemes as perfectly secure. A few such schemes have been proposed in recent literature, but they have vanishing rate. We prove that communication performance can potentially be vastly improved; specifically, our basic setup assumes independently and identically distributed (i.i.d.) covertext, and we construct perfectly secure steganographic codes from public watermarking codes using binning methods and randomized permutations of the code. The permutation is a secret key shared between encoder and decoder. We derive (positive) capacity and random-coding exponents for perfectly-secure steganographic systems. The error exponents provide estimates of the code length required to achieve a target low error probability. We address the potential loss in communication performance due to the perfect-security requirement. This loss is the same as the loss obtained under a weaker order-1 steganographic requirement that would just require matching of first-order marginals of the covertext and stegotext distributions. Furthermore, no loss occurs if the covertext distribution is uniform and the distortion metric is cyclically symmetric; steganographic capacity is then achieved by randomized linear codes. Our framework may also be useful for developing computationally secure steganographic systems that have near-optimal communication performance.Comment: To appear in IEEE Trans. on Information Theory, June 2008; ignore Version 2 as the file was corrupte

Information similarity metrics in information security and forensics

We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken