Detecting Double JPEG Compressed Color Images With the Same Quantization Matrix in Spherical Coordinates

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 8; pp. 2736 - 2749
• Main Authors: Wang, Jinwei, Wang, Hao, Li, Jian, Luo, Xiangyang, Shi, Yun-Qing, Jha, Sunil Kumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Channels; Classification; Color; Color image forensics; Color imagery; Compressing; Conversion; conversion error; double JPEG compression; Feature extraction; Finite wordlength effects; Image coding; Image color analysis; Image compression; JPEG encoders-decoders; Measurement; Pixels; Quantization (signal); Recursive methods; Rounding; rounding error on a pixel; Spherical coordinates; Support vector machines; Transform coding; truncation error on a pixel; Truncation errors
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2019.2922309

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.