DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
Chunlei Peng, Huiqing Guo, Decheng Liu, Nannan Wang, Ruimin Hu, Xinbo, Gao

TL;DR
DeepFidelity introduces a novel face forgery detection framework that assesses perceptual fidelity to improve accuracy across varying face image qualities, utilizing a specialized transformer network and outperforming existing methods.
Contribution
The paper proposes DeepFidelity, a new deepfake detection approach that incorporates perceptual fidelity assessment and a symmetric spatial attention transformer to handle diverse face image qualities.
Findings
Outperforms state-of-the-art methods on benchmark datasets.
Effectively distinguishes real and fake faces across different image qualities.
Enhances detection accuracy by modeling perceptual forgery fidelity.
Abstract
Deepfake detection refers to detecting artificially generated or edited faces in images or videos, which plays an essential role in visual information security. Despite promising progress in recent years, Deepfake detection remains a challenging problem due to the complexity and variability of face forgery techniques. Existing Deepfake detection methods are often devoted to extracting features by designing sophisticated networks but ignore the influence of perceptual quality of faces. Considering the complexity of the quality distribution of both real and fake faces, we propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces with varying image quality by mining the perceptual forgery fidelity of face images. Specifically, we improve the model's ability to identify complex samples by mapping real and fake face data of different…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Face recognition and analysis
MethodsAttention Is All You Need · Label Smoothing · Byte Pair Encoding · Dropout · Linear Layer · Dense Connections · Position-Wise Feed-Forward Layer · Adam · Absolute Position Encodings · Softmax
