Robust deepfake detector against deep image watermarking

Deepfake technology poses a significant threat to information security,rendering deepfake detection research crucial. However, current detection methods experience a marked performance degradation in the presence of deep watermarking within images. In this paper, we propose a multi-module model, which integrates Efficient Multi-scale Attention within Xception as the detection module and introduces a feature dropout module to eliminate redundant image features. Experimental results demonstrate that when 50% and 100% of the images in the dataset contain MBRS watermarks, the accuracy (ACC) metrics of our model are comparable to those of existingbaseline models. However, when 50% and 100% of the images contain FaceSigns watermarks, the ACC metrics of our model outperform those of other baseline models by approximately 10% and 20%, respectively.