关键词: 图像取证, 图像恢复, 篡改检测, 对比学习, 小波特征增强

Abstract: Image tampering detection has become critically important for digital forensics and media content verification. However, in real-world applications, images often undergo post-processing such as brightness and contrast adjustments, which can weaken tampering traces and degrade the performance of existing algorithms. To address this challenge, a novel image tampering detection network ReConWave-Net is proposed. The network consists of two key modules: a classification-guided restoration module that adapts to specific post processing types to counteract brightness and contrast distortions, and a tampering localization module that leverages multi-scale wavelet features and a contrastive learning mechanism to enhance the representation and localization of forged regions. The method was evaluated on multiple datasets under various brightness and contrast perturbations. In terms of restoration quality, the average PSNR increased from about 10 dB to 31 dB, and SSIM improved from 0.40 to 0.92; under typical perturbations, the detection performance reached an F1 score of 0.73 and an IOU score of 0.65. The results indicate that combining targeted restoration with detection can significantly enhance the robustness of forgery localization in post-processed images.

Key words: image forensics, imaestoration, tampering detection, contrastive learning, wavelet feature enhancement