Revealing Photoshop Inpainting Traces Under JPEG Compressions

Photoshop inpainting has become one of the most challenging targets in image forensics, as its content-aware and patch-based editing mechanisms produce visually coherent manipulations with weak and localized forensic artifacts. This difficulty is further amplified by JPEG compression, which is routinely introduced during online transmission and tends to suppress the high-frequency tampering traces on which existing forensic detectors largely depend. As a result, current methods face an inherent trade-off: Photoshop-oriented detectors provide strong discriminability under clean conditions but lack robustness to compression, whereas compression-robust forensic methods often fail to capture subtle inpainting artifacts. To overcome this tension, this paper proposes a JPEG-resistant Photoshop inpainting localization method based on multi-frequency representation. The proposed framework employs a set of parameterized frequency-selective filters to extract complementary representations across multiple spectral bands. Each frequency branch is trained independently as a dedicated detector, and a fusion module integrates their outputs to generate a comprehensive localization map that balances discriminability and robustness. A theoretical analysis in the frequency domain is further provided to explain how low-frequency representations remain stable under JPEG-induced attenuation, supporting the design rationale of the proposed framework. In addition, a multi-quality JPEG augmentation strategy is adopted during training to mitigate the mismatch between training and testing compression levels. Extensive experiments on both script-created and hand-created Photoshop inpainting datasets demonstrate that the proposed method consistently outperforms representative forgery localization methods under various JPEG compression strengths. We further evaluate the method on images transmitted through Wechat, Weibo, and Twitter, confirming its effectiveness in practical online social network scenarios. These results demonstrate that the proposed multi-frequency representation strategy offers a principled and effective approach to robust image forensic analysis.