Detecting Steganographic Content on the Internet

By Niels Provos, Peter Honeyman. Network and Distributed System Security Symposium, 2002

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9dd39782f6d21f8629124304109fa2f9a6a85f35

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1. Songtao Wu
2. Bin Li
3. Sheng-Hua Zhong
4. Jiwu Huang
5. Yan Liu
6. Abbas Cheddad
7. Y. Shi
8. Junhui He

Citations in the corpus

• Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines (2002, background): “Previous approaches to detecting such deviations [11, 28, 17] typically examine first-order statistical distributions of intensity or transform coefficients (e.”
• Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes (2004, background): “The original version of this attack could detect sequentially embedded messages and was later generalized to randomly scattered messages [4,5].”
• Digital image steganography: Survey and analysis of current methods (2010, methodology): “[3,47,48] suggest applying an extended version of the X(2)-test to select Pseudo-randomly embedded messages in JPEG images.”
• A Survey on Image Steganography and Steganalysis (2011, methodology): “Note that some steganalytic methods, for example, the Chi-square attack [17, 40], are effective to LSB steganography for spatial images as well as JPEG images.”
• Hiding Intrusions: From the Abnormal to the Normal and Beyond (2002, background): “In todays world of networked systems, it could also be someone who has legitimate access to systems on the network, but not to the target of the attack.”
• Deep residual learning for image steganalysis (2018, background): “These two techniques are widely used in many important fields, such as the commercial communication and the military communication [7, 25].”
• Detecting hidden messages using higher-order statistical models (2002, methodology): “Previous approaches to detecting such deviations [5, 7, 21, 13 ] typically examine first-order statistical distributions of intensity or transform coefficients (e.g., discrete cosine transform, DCT).”
• Steganalysis of JPEG Images: Breaking the F5 Algorithm (2002, background): “Pfitzmann [12] and Provos [13] noted that the method could still be applied to randomly scattered messages by applying the same idea to smaller portions of the image while comparing the statistics with the one obtained from unrelated pairs of values.”