No abstract

ace forgery detection aims to distinguish between real and fake facial images or videos by identifying manipulated or forged visual media. The main challenge in face forgery detection is achieving high model generalization ability, i.e., satisfactory performance under cross-database scenarios where the training and testing datasets are from different forgery methods. To achieve this goal, this paper presents an Attention-Erasing Stripe Pyramid Network (ASPNet) to utilize high-frequency noises and exploit both the RGB and fine-grained frequency clues. First, since separately extracting features from different scales and granularities will ignore their complementarity, we employ a Stripe Pyramid Block (SPB) to learn multi-scale and multi-granularity features simultaneously. Second, to make the model to focus on useful information and suppress noise, a Two-Stage Attention Block (TSAB) is introduced by combining spatial attention and channel attention to filter out the pixel-wise and channel-wise noise in the learned feature maps. Finally, to dynamically guide the model to pay attention to different areas of the human face, an Attention Erasing (AE) scheme is adopted by randomly erasing units in attention maps. Sufficient experiments demonstrate that ASPNet has superior performance than F3-Net on the FaceForensics++ dataset. The Area Under the Receiver Operating Characteristic Curve (AUC) and the Accuracy (ACC) of our model reach 77.4% and 70.85% respectively, whichare improved by 0.83% and 1.28% compared with F3-Net. Our code is available at: https://github.com/NWPU-Zwu.