Model Attribution of Face-Swap Deepfake Videos

Traditionally, single image super-resolution (SISR) methods randomly crop fixed-size patches in both low and high resolution (LR and HR) images as training samples, and obtain reconstruction model through the regression of LR-HR pixels pairs. However, these will lead to two problems. One is the negligence of the essential information of textures and edges leading to redundant and inefficient training. The other is the lack of the overall perception of data distribution causing poor generalization. To mitigate these issues, we propose Active Sampling and Energy-Based Single Image Super-Resolution (AEBSR), which introduces Active Sampling (AS) and Energy-Based Training (EBT) into SISR. Specifically, we first actively sample texture and edge patches through information entropy, and then align different data distributions between SR and HR images through free energy to perceive the overall distribution characteristics. Extensive experiments show that AS and EBT can further improve the SISR effect and our AEBSR also achieves competitive results compared to the current state-of-the-art SISR approaches.