.png){kind=icon}
返回
MAP-Mamba: Multi-Artifacts Perception Mamba for Generalizable Face Forgery Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2026.3651948.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Face forgery detection suffers from cross-dataset generalization challenges, where performance degradation occurs due to distribution shifts between training and testing data. Recently, pseudo-fake face generation strategy has mitigated models overfitting to specific forgery traces. However, detectors based on this strategy exhibit an overreliance on blending boundary artifacts for their classification decisions. This overreliance significantly limits their ability to generalize to more advanced face manipulation algorithms, such as FaceDancer and InSwap, which are designed to produce smooth and natural transitions in the blending boundary region. To address this, we propose MAP-Mamba, a novel Multi-Artifacts Perception Mamba framework for modeling generalizable artifact representations from “Generation” to “Enrichment” to “Strengthening”. First, we design an attribute-level face blending method that generate pseudo-fake faces containing fine-grained artifacts via three attribute generators. These pseudo-fakes mimic subtle local inconsistencies in advanced forgery algorithms, guiding the MAP-Mamba to learn diverse forgery features beyond the blending boundary artifacts. Second, considering the variability of face artifacts distribution caused by different forgery algorithms, an artifact style mixing strategy is designed to enrich the artifact style distribution in the training phase by mixing and reorganizing the artifact style features, and to enhance the model’s ability to handle unknown forgery methods. Finally, an adaptive artifact guidance mechanism is proposed to dynamically amplify the artifact-related feature to further strengthen the model’s sensitivity to key artifacts. Extensive experiments on several benchmarks show that MAP-Mamba achieves superior robustness and generalization performance.
Keyword:
Face forgery detection
state space model
generalization
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
分享收藏Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection超越先前的伪造知识: 挖掘一般人脸伪造检测的关键线索
OAAI
OAAI分享收藏Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain空间相位浅层学习: 频域人脸伪造检测的再思考OAAI
OAAI分享收藏Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake DetectionOAAI
OAAI分享收藏
Haoyang He, Jiangning Zhang, Yuxuan Cai, Hongxu Chen, Xiaobin Hu, Zhenye Gan, Yabiao Wang, Chengjie Wang, Yunsheng Wu, and Lei Xie. Mobilemamba: Lightweight multi-receptive visual mamba network. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 4497–4507, 2025.何昊阳、张江宁、蔡雨璇、陈宏旭、胡小斌、甘振烨、王亚飙、王成杰、吴云升和谢磊。Mobilemamba:轻量级多感受野视觉Mamba网络。载于计算机视觉与模式识别会议论文集，第4497-4507页，2025年。.png)
IF0
PREAI
分享收藏
查看更多内容