Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
arXiv:2007.09355
Abstract
As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of frequency could be a cure, as frequency provides a complementary viewpoint where either subtle forgery artifacts or compression errors could be well described. To introduce frequency into the face forgery detection, we propose a novel Frequency in Face Forgery Network (F3-Net), taking advantages of two different but complementary frequency-aware clues, 1) frequency-aware decomposed image components, and 2) local frequency statistics, to deeply mine the forgery patterns via our two-stream collaborative learning framework. We apply DCT as the applied frequency-domain transformation. Through comprehensive studies, we show that the proposed F3-Net significantly outperforms competing state-of-the-art methods on all compression qualities in the challenging FaceForensics++ dataset, especially wins a big lead upon low-quality media.
arXiv comment21 pages, 9 figures, accepted as a POSTER at ECCV2020, UPDATE the appendix of the paper
References in corpus (16)
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- The Kinetics Human Action Video Dataset
- MesoNet: a Compact Facial Video Forgery Detection Network
- SGDR: Stochastic Gradient Descent with Warm Restarts
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
- Identification of Deep Network Generated Images Using Disparities in Color Components
- Unmasking DeepFakes with simple Features
- Use of a Capsule Network to Detect Fake Images and Videos
- Wavelet Convolutional Neural Networks for Texture Classification
- Detecting GAN-generated Imagery using Color Cues
- Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos
- Detecting and Simulating Artifacts in GAN Fake Images
- Face X-ray for More General Face Forgery Detection
- Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing
- FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network