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most citedDecoupling Forgery Semantics for Generalizable Deepfake Detection

6 citations · 9 across the 7 of their papers we have counts for

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cs.CV2026

DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis

Feng Ding, Wenhui Yi, Xinan He +3

Generative models now produce imperceptible, fine-grained manipulated faces, posing significant privacy risks. However, existing AI-generated face datasets generally lack focus on…

cs.CV2025

Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection

Feng Ding, Wenhui Yi, Yunpeng Zhou +3

Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward diffe…

cs.CV2025★ 2 cited

VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models

Xinan He, Yue Zhou, Bing Fan +3

Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only…

cs.CV2024

FairAdapter: Detecting AI-generated Images with Improved Fairness

Feng Ding, Jun Zhang, Xinan He +1

The high-quality, realistic images generated by generative models pose significant challenges for exposing them.So far, data-driven deep neural networks have been justified as the…

cs.CV2024★ 6 cited

Decoupling Forgery Semantics for Generalizable Deepfake Detection

Wei Ye, Xinan He, Feng Ding

In this paper, we propose a novel method for detecting DeepFakes, enhancing the generalization of detection through semantic decoupling. There are now multiple DeepFake forgery tec…

cs.CV2024★ 1 cited

Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection

Xinan He, Yue Zhou, Shu Hu +3

Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often…

cs.CV2024

Preserving Fairness Generalization in Deepfake Detection

Li Lin, Xinan He, Yan Ju +3

Although effective deepfake detection models have been developed in recent years, recent studies have revealed that these models can result in unfair performance disparities among…