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Survey of Image Steganography and Steganalysis
Published in Frank Y. Shih, Multimedia Security, 2017
Steganalysis is the process used to detect secret information embedded in images through steganography. Most techniques used in steganography alter the characteristics and statistics of the cover image in some way (Provos and Honeyman, 2002; Kessler, 2004). Statistical analysis of images can detect if an image has been modified with steganography. In correspondence with the steganographic techniques, steganalysis systems fall into the same two broad categories: spatial-domain steganalytic systems (SDSS) and frequency-domain steganalytic systems (FDSS). SDSS is used to analyze characteristics in the spatial-domain image statistics. FDSS is used to analyze characteristics in the frequency-domain image statistics (Wu and Shih, 2006).
Introduction
Published in Frank Y. Shih, Digital Watermarking and Steganography: Fundamentals and Techniques, 2017
Digital steganography aims at hiding digital information in covert channels so that one can conceal the information and prevent the detection of the hidden message. Steganalysis is the art of discovering the existence of hidden information; as such, steganalytic systems are used to detect whether an image contains a hidden message. By analyzing the various features of stego-images (those containing hidden messages) and cover images (those containing no hidden messages), a steganalytic system is able to detect stego-images. Cryptography is the practice of scrambling a message into an obscured form to prevent others from understanding it, while steganography is the practice of obscuring the message so that it cannot be discovered.
A Paradigm Shift for Computational Excellence from Traditional Machine Learning to Modern Deep Learning-Based Image Steganalysis
Published in Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, Data Science and Innovations for Intelligent Systems, 2021
Neelam Swarnkar, Arpana Rawal, Gulab Patel
The overwhelming advancements in network technologies and the presence of enormous data volumes in Internet communication channels in the recent decades has made information more prone to be eavesdropped, impersonated, illegally accessed, or tampered with and hence its secure communication has become a threat across digital communication channels. Steganography refers to the technique of hiding data in multimedia files (text, image, audio, and video) in order to conceal its very existence. Unlike steganography, steganalysis is the practice of detecting the presence of the secret (hidden) data inside the aforementioned multimedia files. These two techniques have their real-time applications in commercial communications for controlling copyright using watermarking (Katzenbeisser & Petitcolas, 1), to prevent illegal copying of content, to detect fraudulent identity cards by identifying the tampered images, to prevent leakage of confidential information in public and private sector to secure patients data in medical sciences, and are also used by security agencies (Balu, Babu, & Amudha, 2018) for transmitting confidential information. It has also been used by terrorists for intra-group communication like the 9/11 attack after which US officials claimed that Al-Qaeda used steganography for covert communication (Avcibas, Memon, & Sankur, 3), terrorists of Boko-haram sect in Nigeria used steganographic schemes for transferring secret information (Katzenbeisser & Petitcolas, 1), and an Al-Qaeda suspect in 2011 was arrested with a chip having porn video inside with 141 text files consisting of their invasions were hidden using steganography (Kolade, Olayinka, Sunday, Adesoji, & Olubusola, 4) to mention a few.
USAD: undetectable steganographic approach in DCT domain
Published in The Imaging Science Journal, 2019
Marwa Saidi, Olfa Mannai, Houcemeddine Hermassi, Rhouma Rhouma, Safya Belghith
Machine learning-based steganalysis approaches have been widely considered as powerful tools regarding to the possibility of extending them into multi-class detection and payload estimation. To ensure higher detection accuracy in digital steganography, we have to bear in mind the dependency of the feature extractor on the used steganographic approach. Feature-based steganalysis works systematically by adopting a specific model of the cover support, then it ensures the training, the testing and the validation phases using machine learning algorithms.