Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 30, Issue 8, Pages 2736-2749Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2019.2922309
Keywords
Transform coding; Image coding; Quantization (signal); Color; Feature extraction; Image color analysis; Finite wordlength effects; Color image forensics; double JPEG compression; spherical coordinates; conversion error; truncation error on a pixel; rounding error on a pixel
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Funding
- National Natural Science Foundation of China [61772281, 61702235, U1636117, U1636219, 61502241, 61402235, 61572258]
- National Key Research and Development Program of China [2016YFB0801303, 2016QY01W0105]
- plan for Scientific Talent of Henan Province [2018JR0018]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund
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Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
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