深度伪造取证适配器：一种通用深度伪造检测的双流网络
Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection
AI总结：
本文提出深度伪造取证适配器，通过双流网络结合CLIP模型实现高效通用深度伪造检测。
摘要：
深度伪造生成技术的快速进步对公共安全构成了重大威胁，并通过创建高度逼真的合成面部媒体造成社会损害。尽管现有检测方法在泛化到新兴伪造模式时表现出局限性，本文提出了深度伪造取证适配器（DFA），一种新颖的双流框架，通过结合视觉-语言基础模型与针对性的取证分析。我们的方法整合了预训练的CLIP模型，结合三个核心组件，通过利用CLIP的强大通用能力（不更改CLIP参数）实现专门的深度伪造检测：1）全局特征适配器用于识别图像内容中的全局不一致之处，可能表明伪造；2）局部异常流通过显式利用面部结构先验增强模型感知局部面部伪造线索的能力；3）交互融合分类器通过变压器编码器促进全局和局部特征之间的深度交互和融合。对帧级和视频级基准的广泛评估证明了DFA的优越泛化能力，特别是在具有挑战性的DFDC数据集中，帧级AUC/EER达到0.816/0.256，视频级AUC/EER达到0.836/0.251，比先前方法在视频AUC上提高了4.8%。我们的框架不仅展示了最先进的性能，还指出了开发具有增强泛化能力的稳健深度伪造检测系统可行且有效的发展方向。我们的代码可在https://github.com/Liao330/DFA.git获取。
英文摘要：
The rapid advancement of deepfake generation techniques poses significant threats to public safety and causes societal harm through the creation of highly realistic synthetic facial media. While existing detection methods demonstrate limitations in generalizing to emerging forgery patterns, this paper presents Deepfake Forensics Adapter (DFA), a novel dual-stream framework that synergizes vision-language foundation models with targeted forensics analysis. Our approach integrates a pre-trained CLIP model with three core components to achieve specialized deepfake detection by leveraging the powerful general capabilities of CLIP without changing CLIP parameters: 1) A Global Feature Adapter is used to identify global inconsistencies in image content that may indicate forgery, 2) A Local Anomaly Stream enhances the model's ability to perceive local facial forgery cues by explicitly leveraging facial structure priors, and 3) An Interactive Fusion Classifier promotes deep interaction and fusion between global and local features using a transformer encoder. Extensive evaluations of frame-level and video-level benchmarks demonstrate the superior generalization capabilities of DFA, particularly achieving state-of-the-art performance in the challenging DFDC dataset with frame-level AUC/EER of 0.816/0.256 and video-level AUC/EER of 0.836/0.251, representing a 4.8% video AUC improvement over previous methods. Our framework not only demonstrates state-of-the-art performance, but also points out a feasible and effective direction for developing a robust deepfake detection system with enhanced generalization capabilities against the evolving deepfake threats. Our code is available at https://github.com/Liao330/DFA.git
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
