Backdoor data detection is traditionally studied in an end-to-end supervised learning (SL) setting. However, recent years have seen the proliferating adoption of self-supervised learning (SSL) and transfer learning (TL), due to their lesser need for labeled data. Successful backdoor attacks have also been demonstrated in these new settings. However, we lack a thorough understanding of the applicability of existing detection methods across a variety of learning settings. By evaluating 56 attack settings, we show that the performance of most existing detection methods varies significantly across different attacks and poison ratios, and all fail on the state-of-the-art clean-label attack. In addition, they either become inapplicable or suffer large performance losses when applied to SSL and TL. We propose a new detection method called Active Separation via Offset (ASSET), which actively induces different model behaviors between the backdoor and clean samples to promote their separation. We also provide procedures to adaptively select the number of suspicious points to remove. In the end-to-end SL setting, ASSET is superior to existing methods in terms of consistency of defensive performance across different attacks and robustness to changes in poison ratios; in particular, it is the only method that can detect the state-of-the-art clean-label attack. Moreover, ASSET's average detection rates are higher than the best existing methods in SSL and TL, respectively, by 69.3% and 33.2%, thus providing the first practical backdoor defense for these new DL settings. We open-source the project to drive further development and encourage engagement: https://github.com/ruoxi-jia-group/ASSET.

翻译：后门数据检测传统上是在端到端监督学习背景下研究的。然而，近年来，由于自监督学习和迁移学习对标注数据的需求较低，其应用日益普及。在这些新场景中，成功的后门攻击已被证实。然而，我们尚缺乏对现有检测方法在不同学习设置中适用性的深入理解。通过评估56种攻击场景，我们发现大多数现有检测方法在不同攻击和投毒比例下性能差异显著，且全部在先进的无标签攻击上失效。此外，它们应用于SSL和TL时要么不适用，要么性能大幅下降。我们提出了一种名为“通过偏移主动分离”（ASSET）的新型检测方法，该方法通过主动诱导后门与干净样本间不同的模型行为来促进两者分离。我们还提供了自适应选择可疑点数量的程序。在端到端SL设置中，ASSET在不同攻击间的防御性能一致性及对投毒比例变化的鲁棒性均优于现有方法；特别是，它是唯一能检测先进无标签攻击的方法。此外，ASSET在SSL和TL下的平均检测率分别比现有最佳方法高出69.3%和33.2%，从而为这些新型深度学习场景提供了首个实用的后门防御方案。我们开源该项目以推动进一步发展并鼓励参与：https://github.com/ruoxi-jia-group/ASSET。