Constrained R-CNN: A general image manipulation detection model
This addresses image forensics for detecting manipulated images, offering a more complete solution but appears incremental as it builds on existing R-CNN architectures.
The paper tackles the problem of poor universality and lack of classification in image manipulation detection by proposing Constrained R-CNN, achieving state-of-the-art performance with F1 score increases of 28.4%, 73.2%, and 13.3% on NIST16, COVERAGE, and Columbia datasets.
Recently, deep learning-based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these issues, we propose a coarse-to-fine architecture named Constrained R-CNN for complete and accurate image forensics. First, the learnable manipulation feature extractor learns a unified feature representation directly from data. Second, the attention region proposal network effectively discriminates manipulated regions for the next manipulation classification and coarse localization. Then, the skip structure fuses low-level and high-level information to refine the global manipulation features. Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region. Experimental results show that our model achieves state-of-the-art performance. Especially, the F1 score is increased by 28.4%, 73.2%, 13.3% on the NIST16, COVERAGE, and Columbia dataset.