Forgery detection using polynomial fitting in recompressed JPEG images

• Published in: Signal, image and video processing image and video processing, 2024-04, Vol.18 (3), p.2439-2451
• Main Authors: Chai, Xiuli, Tan, Yong, Gan, Zhihua, Niu, Yakun, Wang, Jinwei
• Format: Article
• Language: English
• Subjects: Color; Computer Imaging; Computer Science; Convergence; Curve fitting; Image compression; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Polynomials; Signal,Image and Speech Processing; Support vector machines; Truncation errors; Vision
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-023-02919-y

Detection of double Joint photographic experts group (JPEG) compression is a major part of image forensics, especially double JPEG compression detection with the same quantization matrix is still a challenging problem. Detection of double JPEG compression for different quantization matrices has achieved good performance so far, and some methods for detecting the same quantization matrix have been proposed. However, most methods designed for grayscale images, where they utilize only truncation and rounding errors, not fully exploiting all the useful information of color JPEG images. In addition, there is no suitable model to analyze the convergence of JPEG images. These results in poor accuracy of the double compression detection for color JPEG images with the same quantization matrix. To solve this problem, we propose an effective method that utilizes third-order polynomial fitting for error convergence curves. Firstly, five successive compressions are used to extract the quantization error, dequantization error, coefficient error, and truncation error in each compression and decompression process. Secondly, based on the convergence characteristics of these four types of error information, a third-order polynomial model and least squares are applied for the fit, and then, the slope of each point of the fitted curve and the difference of the slopes are used as convergence characteristics. Moreover, the fitted curves predict the pre-compression state, which can effectively amplify the gap between single and double compression. Finally, support vector machines (SVM) are used to train and predict the final features. The experiments illustrate that the proposed method enhances accuracy by 2–3% compared to the most recent method and by approximately 15% compared to the earlier method in multiple datasets, including NRCS, MIXDATA, and MIXDATA-I.