文献类型：期刊文献

英文题名：YOLODF: YOLO-Based Spatial-Frequency Interaction Mining for?General Deepfake Detection

作者：Li, Xin[1,2]; Xu, Bingxin[1,2]; Liu, Hongzhe[1,2]; Pan, Weiguo[1,2]; Xu, Cheng[1,2]

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2026

卷号：16278 LNCS

起止页码：455-470

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20260519980386)

语种：英文

外文关键词：Data mining - Face recognition - Feature extraction - Statistical tests

摘要：With the advancement of face forgery methods, deepfake detection has become a major concern. Recent studies demonstrate outstanding performance in intra-domain scenarios. However, they inevitably encounter generalization challenges when evaluated on unseen data. This paper introduces YOLODF, a novel YOLO-based deepfake detection model that enhances generalizability and mitigates overfitting through optimizations at both the data and feature levels. Specifically, Random Erasing Mask (REM) alleviates model’s undue dependence on inherent data biases via occluding and adding noise in sensitive facial regions. Multi-scale Cross-domain Feature Interaction (MCFI) utilizes the high-pass filter to explore and fusion multi-scale anomalies in local regions. Moreover, Domain Invariant Clues Learning (DICL) strengthens the learning of intrinsic representations by reconstructing the low-frequency distribution and optimizing crucial local features. We extensively evaluate and compare YOLODF for both in-dataset and cross-dataset tests on five public datasets. Experiment results confirm that our method can achieve a state-of-the-art generalization performance. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.