DiffusionFF: Face Forgery Detection via Diffusion-based Artifact Localization
This addresses the need for robust and explainable deepfake detection to enhance user trust, though it appears incremental as it builds on existing methods.
The paper tackles face forgery detection by proposing DiffusionFF, a framework that uses diffusion models to localize artifacts, resulting in significant improvements in detection accuracy on cross-dataset and intra-dataset benchmarks.
The rapid evolution of deepfake generation techniques demands robust and accurate face forgery detection algorithms. While determining whether an image has been manipulated remains essential, the ability to precisely localize forgery artifacts has become increasingly important for improving model explainability and fostering user trust. To address this challenge, we propose DiffusionFF, a novel framework that enhances face forgery detection through diffusion-based artifact localization. Our method utilizes a denoising diffusion model to generate high-quality Structural Dissimilarity (DSSIM) maps, which effectively capture subtle traces of manipulation. These DSSIM maps are then fused with high-level semantic features extracted by a pretrained forgery detector, leading to significant improvements in detection accuracy. Extensive experiments on both cross-dataset and intra-dataset benchmarks demonstrate that DiffusionFF not only achieves superior detection performance but also offers precise and fine-grained artifact localization, highlighting its overall effectiveness.