Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and
  Localization
Yingxin Lai Zhiming Luo Zitong Yu
The rapid advancements in computer vision have stimulated remarkable progress
in face forgery techniques, capturing the dedicated attention of researchers
committed to detecting forgeries and precisely localizing manipulated areas.
Nonetheless, with limited fine-grained pixel-wise supervision labels, deepfake
detection models perform unsatisfactorily on precise forgery detection and
localization. To address this challenge, we introduce the well-trained vision
segmentation foundation model, i.e., Segment Anything Model (SAM) in face
forgery detection and localization. Based on SAM, we propose the Detect Any
Deepfakes (DADF) framework with the Multiscale Adapter, which can capture
short- and long-range forgery contexts for efficient fine-tuning. Moreover, to
better identify forged traces and augment the model's sensitivity towards
forgery regions, Reconstruction Guided Attention (RGA) module is proposed. The
proposed framework seamlessly integrates end-to-end forgery localization and
detection optimization. Extensive experiments on three benchmark datasets
demonstrate the superiority of our approach for both forgery detection and
localization. The codes will be released soon at
https://github.com/laiyingxin2/DADF.