29 June 2017 High-quality JPEG compression history detection for fake uncompressed images
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Authenticity is one of the most important evaluation factors of images for photography competitions or journalism. Unusual compression history of an image often implies the illicit intent of its author. Our work aims at distinguishing real uncompressed images from fake uncompressed images that are saved in uncompressed formats but have been previously compressed. To detect the potential image JPEG compression, we analyze the JPEG compression artifacts based on the tetrolet covering, which corresponds to the local image geometrical structure. Since the compression can alter the structure information, the tetrolet covering indexes may be changed if a compression is performed on the test image. Such changes can provide valuable clues about the image compression history. To be specific, the test image is first compressed with different quality factors to generate a set of temporary images. Then, the test image is compared with each temporary image block-by-block to investigate whether the tetrolet covering index of each 4 × 4 block is different between them. The percentages of the changed tetrolet covering indexes corresponding to the quality factors (from low to high) are computed and used to form the p -curve, the local minimum of which may indicate the potential compression. Our experimental results demonstrate the advantage of our method to detect JPEG compressions of high quality, even the highest quality factors such as 98, 99, or 100 of the standard JPEG compression, from uncompressed-format images. At the same time, our detection algorithm can accurately identify the corresponding compression quality factor.
© 2017 SPIE and IS&T
Rong Zhang, Rang-Ding Wang, Li-Jun Guo, Bao-Chuan Jiang, "High-quality JPEG compression history detection for fake uncompressed images," Journal of Electronic Imaging 26(3), 033028 (29 June 2017). https://doi.org/10.1117/1.JEI.26.3.033028 . Submission: Received: 28 December 2016; Accepted: 9 June 2017
Received: 28 December 2016; Accepted: 9 June 2017; Published: 29 June 2017

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