揭秘影响深度伪造检测效果的关键实现细节。
Generalized Design Choices for Deepfake Detectors
- 系统分析训练、推理与增量更新中的设计选择
- 发现一组能提升检测性能的通用实践
- 适合构建可靠检测系统的研究者与开发者
深度伪造检测方法的有效性往往不取决于核心架构，而更多受数据预处理、增强策略和优化技术等实现细节影响。这些因素使得检测器之间的公平比较困难，也难以判断哪些因素真正推动了性能提升。为此，我们系统研究不同设计选择对深度伪造检测模型准确率和泛化能力的影响，重点关注训练、推理及增量更新环节。通过隔离各因素的独立影响，旨在建立与模型架构无关的稳健最佳实践，为未来检测系统的设计与开发提供指导。实验识别出一组可持续提升检测性能的设计选择，并在AI-GenBench基准上实现当前最优表现。
原文摘要 · Abstract (English)
The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and optimization techniques. These factors make it difficult to fairly compare detectors and to understand which factors truly contribute to their performance. To address this, we systematically investigate how different design choices influence the accuracy and generalization capabilities of deepfake detection models, focusing on aspects related to training, inference, and incremental updates. By isolating the impact of individual factors, we aim to establish robust, architecture-agnostic best practices for the design and development of future deepfake detection systems. Our experiments identify a set of design choices that consistently improve deepfake detection and enable state-of-the-art performance on the AI-GenBench benchmark.
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