Improved Detection of Forged and Generated Facial Images Based on ResNet-50

Detecting forged and generated images has recently grown into an emerging research area. As forgery and generation technologies advance, they pose risks of personal privacy and public security. Existing algorithms are designed to detect either forged or generated facial images. Due to a lack of generalizability, their performance usually degrades when faced with a mixture of both types. To tackle this problem, this paper proposes a framework Res50_Attn_DSCE that enhances generalizability and extracts both local and global features, thereby improving the algorithm's performance across different types of datasets. Additionally, depth-separable convolution reduces computational costs. Experimental results demonstrate that our proposed model achieves the highest accuracy with the shortest runtime. Compared to other traditional algorithms, these results validate the effectiveness of our model's improvements.