![]() ![]() Steganalysis, the process to detect the presence of the hidden data/message, has two major components feature extraction and classification. Every image entering the system is tested for steganography. It keeps track of user's entire file system and detects arrival of new images in the system. Once installed on a machine, StegTrack always remains active. To the best of our knowledge such a tool does not exist in literature. StegTrack is an antivirus like tool to track steganograms among images on a computer. This paper presents the design and implementation of StegTrack, a novel proactive steganalysis tool. This synergy greatly improves protection of confidential information. The ability of hiding the existence of confidential information comes from steganography and its encryption using a coding table makes its content undecipherable. ![]() Hence, a new method which combines the favourable properties of cryptography based on substitution encryption and stenography is analysed in the paper. However, the stenography also has its disadvantages, listed in the paper. This has led to the development of steganography, a science which attempts to hide the very existence of confidential information. The possibility of decryption has increased with the development of computer technology since available computer speeds enable the decryption process based on the exhaustive data search. Each encryption algorithm can be decrypted within sufficient time and with sufficient resources. However, over time, flaws have been discovered even with the most sophisticated encryption algorithms. Many encryption algorithms have been developed for protection of confidential information. In modern communication systems, one of the most challenging tasks involves the implementation of adequate methods for successful and secure transfer of confidential digital information over an unsecured communication channel. Furthermore, we provide a framework for benchmarking future techniques. We benchmark embedding rate versus detectability performances of several widely used embedding as well as universal steganalysis techniques. The image dataset is categorized with respect to the size and quality. Our experiments are done using a large dataset of JPEG images, obtained by randomly crawling a set of publicly available websites. These universal steganalysis techniques are tested against a number of well know embedding techniques, including Outguess, F5, Model based, and perturbed quantization. In this work, our goal is to compare a number of universal steganalysis techniques proposed in the literature which include techniques based on binary similarity measures, wavelet coefficients' statistics, and DCT based image features. More specifically Universal steganalysis techniques have become more attractive since they work independently of the embedding technique. In turn the development of these techniques have led to an increased interest in steganalysis techniques. There have been a number of steganography embedding techniques proposed over the past few years. ![]()
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