| Paper ID | B-3-2.7 |
| Paper Title |
A Generative Adversarial Network Framework for JPEG Anti-Forensics |
| Authors |
Jianyuan Wu, Sun Yat-Sen University, China; Li Liu, Kwai Incorporated, United States; Xiangui Kang, Sun Yat-Sen University, China; Wei Sun, Sun Yat-sen University, China |
| Session |
B-3-2: Privacy Preserving and Multimedia Security |
| Time | Thursday, 10 December, 15:30 - 17:15 |
| Presentation Time: | Thursday, 10 December, 17:00 - 17:15 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Multimedia Security and Forensics (MSF): Special Session: Privacy Preserving and Multimedia Security |
| Abstract |
JJPEG anti-forensics aims to remove the artifacts left by JPEG compression and recover JPEG compressed images. However, the existing JPEG anti-forensic methods often introduce new traces and cause the degradation of visual quality of the processed images. In this work, JPEG anti-forensics are modelled as an image-to-image translation problem, where a generative adversarial network framework is used to translate a JPEG compressed image to a reconstructed one. Since JPEG compression causes impairment to high-frequency components, a loss function of high-frequency Discrete Cosine Transform (DCT) coefficients is proposed to recover these components. To prevent forensic detection, a calibration loss function is further introduced to mitigate the variance gap in the high-frequency subbands between generated images and their calibrated versions. Our experimental results demonstrate that the proposed method achieves better image quality than the existing state-of-the-art JPEG anti-forensic methods with comparable anti-forensic performance. |