CMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images

The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential documents processed in government departments and commercial companies. However, detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues, such as moiré pattern, are weakened during transmission. In this work, we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then, we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding, we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map, and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the proposed method, we collect a recaptured office document image dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outperforms a SOTA approach (with RGB modality only), reducing the average EER from 26.82% to 16.78%. Robustness evaluation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression (QF=70) and resolution as low as 534×503 pixels.