A method to detect JPEG-based double compression

Authors:
Qingzhong Liu;Andrew H. Sung;Mengyu Qiao
Affiliations:
Department of Computer Science, Sam Houston State University, Huntsville, TX;Department of Computer Science and Socorro, NM and Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, NM;Department of Computer Science and Socorro, NM and Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, NM
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

Digital multimedia forensics is an emerging field that has important applications in law enforcement, the protection of public safety, and notational security. As a popular image compression standard, the JPEG format is widely adopted; however, the tampering of JPEG images can be easily performed without leaving visible clues, and it is increasingly necessary to develop reliable methods to detect forgery in JPEG images. JPEG double compression is frequently used during image forgery, and it leaves a clue to the manipulation. To detect JPEG double compression, we propose in this paper to extract the neighboring joint density features and marginal density features on the DCT coefficients, and then to apply learning classifiers to the features for detection. Experimental results indicate that the proposed method delivers promising performance in uncovering JPEG-based double compression. In addition, we analyze the relationship among compression quality factor, image complexity, and the performance of our double compression detection algorithm, and demonstrate that a complete evaluation of the detection performance of different algorithms should necessarily include both the image complexity and double compression quality factor.