Siva S. Sivatha Sindhu

Evolving GA Classifier for Breaking the Steganographic Utilities: Stools, Steganos and Jsteg

Differentiating anomalous image documents (stego image) from pure image file (cover image) is difficult and tedious. Steganalytic techniques strive to detect whether an image contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to image steganalysis. The basic idea is that, the various image quality metrics calculated on cover image files and on stego-image files vis-a-vis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from image data using these image quality metrics and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the image documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to generally detect any new steganography technique. The implementation of the GA based image steganalyzer relies on the choice of these image quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched image samples. Experimental results show that the proposed technique provides promising detection rates.

StegoBreaker: Audio Steganalysis using Ensemble Autonomous Multi-Agent and Genetic Algorithm

The goal of steganography is to avoid drawing suspicion to the transmission of a hidden message in multi-medium. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper investigates the use of genetic algorithm (GA) to aid autonomous intelligent software agents capable of detecting any hidden information in audio files, automatically. This agent would make up the detection agent in an architecture comprising of several different agents that collaborate together to detect the hidden information. The basic idea is that, the various audio quality metrics (AQMs) calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. GA employs these AQMs to steganalyse the audio data. The overall agent architecture will operate as an automatic target detection (ATD) system. The architecture of ATD system is presented in this paper and it is shown how the detection agent fits into the overall system. The design of ATD based audio steganalyzer relies on the choice of these audio quality measures and the construction of a GA based rule generator, which spawns a set of rules that discriminates between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates

Blind image steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system

This paper reports the design principles and evaluation results of a new experimental universal, blind image steganalysing system. This system investigates the use of content independent statistical evidences left by the steganograms, as features for an image steganalyzer. The work is aimed at maximizing the sensitivity and specificity of the steganalyzer and to accomplish both security and system performance. A genetic-X-means classifier is constructed to realize the proposed model. For performance evaluation, a database composed of 5600 plain and stego images (generated by using seven different embedding schemes) was established. The results of our empirical experiment prove the vitality of the proposed scheme in detecting stego anomalies in images. In addition, the simulation results show that the effectiveness of steganalytic system can be enhanced by considering the content independent distortion measures and maximizing the sensitivity and specificity of the system.

StegoHunter: Steganalysis of LSB Embedded Images Based on Stego-Sensitive Threshold Close Color Pair Signature

This paper proposes a reliable framework for the detection of the least significant bit (LSB) steganography using digital media files as cover objects. Steganographic methods attempt to insert data in multimedia signals in an undetectable fashion. However, these methods often disrupt the underlying signal characteristics, thereby allowing detection under careful steganalysis. Under repeated embedding, disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. This principle is used to derive a steganalysis tool that detects the presence of hidden messages in uncompressed twenty-four bits BMP image. This work presents close color pair analysis with stego-sensitive threshold (CCPASST) to detect stego-objects with even 10% payload. In earlier works 20% payload was detected through close color pair analysis. The new framework exploits the first-order statistics of structural similarity index measure of the samples to calculate the threshold. The literature contains only one other detector specialized with variable threshold, and the one presented here is substantially more sensitive. Simulation results with the stego-sensitive threshold applied on well-known LSB steganographic technique indicate that this approach is superior to the earlier methods and is able with promising accuracy to distinguish between clean and stego images.

An Active Rule Based Approach to Audio Steganalysis with a Genetic Algorithm

Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to audio steganalysis. The basic idea is that, the various audio quality metrics calculated on cover audio signals and on stego audio signals vis-avis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method provides flexibility to generally detect any new steganography technique. The implementation of the GA based audio steganalyzer relies on the choice of these audio quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates.

Geometric Attack Invariant Watermarking with Biometric Data - Applied on Offline Handwritten Signature

Biometric watermarking refers to the process of incorporating the handwritten signatures or fingerprints in watermarking technology. In this paper, we present a novel statistical attack invariant watermarking scheme to embed an offline handwritten signature invisibly in the host as a notice of genuine ownership. The scheme employs Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) with Least Significant Bit (LSB) encoding for watermark insertion, which is to be known as DCT-SVD scheme. Experimental results confirm that DCT-SVD scheme is robust to statistical attacks even in the presence of deliberate distortions.

High Performance Image Steganalysis Through Stego Sensitive Feature Selection Using MBEGA

In recent years steganalysis has emerged as an important branch in information forensics. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviors, optimizing the performance of steganalysers becomes an important open problem. This paper is aimed at increasing the performance of the steganalysers in [1], [2] and [3] through feature selection thereby reducing the computational complexity and increase the classification accuracy of the selected feature subsets. In this study, we propose to employ Markov Blanket-Embedded Genetic Algorithm (MBEGA) for stego sensitive feature selection process. In particular, the embedded Markov blanket based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on [1], [2] and [3] suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. Experimental results prove that the proposed method is superior in terms of number of selected features, classification accuracy, and running time than the existing algorithms.

StegoHunter: Passive audio steganalysis using Audio Quality Metrics and its realization through genetic search and X-means approach

Steganography is used to hide the occurrence of communication. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. This paper investigates the use of a Genetic-X-means classifier, which distinguishes a pure audio document from the adulterated one. The basic idea is that, the various audio quality metrics (AQMs) calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. Our model employs these AQMs to steganalyse the audio data. Genetic paradigm is exploited to select the AQMs that are sensitive to various embedding techniques. The classifier between cover and stego-files is built using X-means clustering on the selected feature set. The presented method can not only detect the presence of hidden message but also identify the hiding domains. The experimental results show that the combination strategy (Genetic-X-means) can improve the classification precision even with lesser payload compared to the traditional ANN (Back Propagation Network).

AQM-based audio steganalysis and its realisation through rule induction with a genetic learner

Steganography, the means for covert communication, creates a potential problem when it is misused for planning criminal activities. Differentiating anomalous audio document from pure audio document is difficult. This paper presents a Genetic Algorithm based approach to audio steganalysis. The basic idea is that the various audio quality metrics calculated on cover audio signals and on stego audio signals vis-a-vis their denoised versions are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and the support-confidence framework is utilised as a fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real-time environment Experimental results show that the proposed technique provides promising detection rates.

Stego-Breaker: Defeating the Steganographic Systems through Genetic-X-Means approach using Image Quality Metrics

Steganography is used to hide the occurrence of communication. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous images (stego image) from pure images (cover image) is difficult and tedious. This paper investigates the use of a Genetic-X-means classifier, which distinguishes a pure image from the adulterated one. The basic idea is that, the various Image Quality Metrics (IQMs) calculated on cover images and on stego-images vis-a-vis their denoised versions, are statistically different. Our model employs these IQMs to steganalyse the image data. Genetic paradigm is exploited to select the IQMs that are sensitive to various embedding techniques. The classifier between cover and stego-files is built using X-means clustering on the selected feature set. The presented method can not only detect the presence of hidden message but also identify the hiding domains. The experimental results show that the combination strategy (Genetic-X-means) can improve the classification precision even with lesser payload compared to the traditional ANN (Back Propagation Network).