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.
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References
CBS News, http://www.cbsnews.com/8301-503543_162-20016679-503543.html
CBS News, http://www.cbsnews.com/stories/2010/09/17/world/main6876519.shtml
National Public Radio, http://www.npr.org/blogs/thetwo-way/2010/09/17/129938169/doctored-photograph-hosni-mubarak-al-ahram-white-house-obama-mideast-peace-talks
Chen, C., Shi, Y., Su, W.: A Machine Learning Based Scheme for Double JPEG Compression Detection. In: Proc. of 19th ICPR, pp. 1–4 (2008)
Cho, D., Bui, T.: Multivariate Statistical Modeling for Image Denoising Using Wavelet Transforms. Signal Processing: Image Communication 20, 77–89 (2005)
Farid, H.: Image Forgery Detection, a Survey. IEEE Signal Processing Magazine, 16–25 (March 2009)
Liu, Q., Sung, A.H.: Feature Mining and Nuero-fuzzy Inference System for Steganalysis of LSB Matching Steganography in Grayscale Images. In: Proc. 20th IJCAI, pp. 2808–2813 (2007)
Liu, Q., Sung, A.H., Chen, H., Xu, J.: Feature Mining and Pattern Classification for Steganalysis of LSB Matching Steganography in Grayscale Images. Pattern Recognition 41(1), 56–66 (2008)
Liu, Q., Sung, A.H., Ribeiro, B.M., Wei, M., Chen, Z., Xu, J.: Image Complexity and Feature Mining for Steganalysis of Least Significant Bit Matching Steganography. Information Sciences 178(1), 21–36 (2008)
Liu, Q., Sung, A.H., Qiao, M.: Novel Stream Mining for Audio Steganalysis. In: Proc. 17th ACM Multimedia, pp. 95–104 (2009)
Liu, Q., Sung, A.H., Qiao, M.: Derivative Based Audio Steganalysis. ACM Trans. Multimedia Computing, Communications and Applications (in press)
Liu, Q., Sung, A.H., Qiao, M.: Neighboring Joint Density Based JPEG Steganalysis. ACM Trans. Intelligent Systems and Technology 2(2), article 16 (2011), doi:10.1145/1899412.1899420
Ohm, J.R.: Multimedia Communication Technology, Representation, Transmission and Identification of Multimedia Signals. Springer, Berlin (2004)
Pevny, T., Fridrich, J.: Detection of Double-compression in JPEG Images for Applications in Steganography. IEEE Trans. Information Forensics and Security 3(2), 247–258 (2008)
Sharifi, K., Leon-Garcia, A.: Estimation of Shape Parameter for Generalized Gaussian Distributions in Subband Decompositions of Video. IEEE Trans. Circuits Syst. Video Technol. 5, 52–56 (1995)
Vapnik, V.: Statistical Learning Theory. John Wiley, Chichester (1998)
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Liu, Q., Sung, A.H., Qiao, M. (2011). A Method to Detect JPEG-Based Double Compression. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_55
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DOI: https://doi.org/10.1007/978-3-642-21090-7_55
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