FakeChain：揭示多步深度伪造检测中的浅层线索
FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection
AI总结：
本研究针对多步混合深度伪造对单步训练检测模型的挑战，推出大型基准FakeChain并开展分析，发现检测性能高度依赖最终篡改类型，凸显了考虑篡改序列及相关基准的重要性。
摘要：
多步或混合深度伪造通过依次应用换脸、基于GAN的生成、扩散方法等不同深度伪造创建手段生成，会给在单步伪造数据上训练的检测模型带来新的、未预见的技术挑战。现有研究主要聚焦于检测孤立的单一篡改，人们对检测模型在这类组合式、混合式复杂篡改流程下的表现仍知之甚少。\n本研究推出大型基准FakeChain，包含采用5种当前最优的代表性生成器合成的1步、2步和3步伪造数据。我们借助该基准分析了不同步骤的混合篡改下的检测性能与频谱特性，同时覆盖不同的生成器组合与质量设置。\n令人意外的是，研究结果显示检测性能高度依赖最终篡改类型：当最终篡改类型与训练分布不一致时，F1分数最高下降58.83%。这清晰表明检测器依赖的是最后阶段的伪影而非累积的篡改痕迹，这限制了模型的泛化能力。这些发现凸显出检测模型需明确考虑篡改历史与序列的必要性，也彰显了FakeChain这类基准的重要性——它们能反映现实场景中日益增长的合成复杂度与多样性。我们的示例代码可在https://github.com/minjihh/FakeChain获取。
英文摘要：
Multi-step or hybrid deepfakes, created by sequentially applying different deepfake creation methods such as Face-Swapping, GAN-based generation, and Diffusion methods, can pose an emerging and unforseen technical challenge for detection models trained on single-step forgeries. While prior studies have mainly focused on detecting isolated single manipulation, little is known about the detection model behavior under such compositional, hybrid, and complex manipulation pipelines. In this work, we introduce \textbf{FakeChain}, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. Using this approach, we analyze detection performance and spectral properties across hybrid manipulation at different step, along with varying generator combinations and quality settings. Surprisingly, our findings reveal that detection performance highly depends on the final manipulation type, with F1-score dropping by up to \textbf{58.83\%} when it differs from training distribution. This clearly demonstrates that detectors rely on last-stage artifacts rather than cumulative manipulation traces, limiting generalization. Such findings highlight the need for detection models to explicitly consider manipulation history and sequences. Our results highlight the importance of benchmarks such as FakeChain, reflecting growing synthesis complexity and diversity in real-world scenarios. Our sample code is available here\footnote{https://github.com/minjihh/FakeChain}.
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。
