论文标题
数据中毒是如何有毒的？用于后门和数据中毒攻击的统一基准
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
论文作者
论文摘要
数据中毒和后门攻击操纵培训数据，以使模型在推理过程中失败。最近对行业从业人员的一项调查发现，数据中毒是从模型窃取到对抗性攻击的威胁中的首要关注点。但是，考虑到这些方法，即使是相同目标的方法，尚未在一致或逼真的环境中进行测试，尚不清楚哪种方法的危险中毒方法有多危险。我们观察到数据中毒和后门攻击对测试设置的变化非常敏感。此外，我们发现现有方法可能无法推广到现实的设置。尽管这些现有作品是数据中毒的有价值的原型，但我们采用严格的测试来确定我们应该害怕它们的程度。为了促进未来工作的公平比较，我们为数据中毒和后门攻击开发了标准化的基准。
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference. A recent survey of industry practitioners found that data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. However, it remains unclear exactly how dangerous poisoning methods are and which ones are more effective considering that these methods, even ones with identical objectives, have not been tested in consistent or realistic settings. We observe that data poisoning and backdoor attacks are highly sensitive to variations in the testing setup. Moreover, we find that existing methods may not generalize to realistic settings. While these existing works serve as valuable prototypes for data poisoning, we apply rigorous tests to determine the extent to which we should fear them. In order to promote fair comparison in future work, we develop standardized benchmarks for data poisoning and backdoor attacks.
