提炼不可提炼的东西:向一个讨厌的老师学习

Surgan Jandial, Yash Khasbage, Arghya Pal, V. Balasubramanian, Balaji Krishnamurthy
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引用次数: 2

摘要

最近,利用知识蒸馏(Knowledge Distillation, KD)技术无意中窃取私人/敏感信息的行为引起了人们的极大关注,并指导了后续的防御工作。最近的作品《讨厌的老师》提出要培养那些不能被攻击它的模式所提炼和模仿的教师。然而,一个讨厌的老师所提供的保密承诺并没有得到很好的研究,作为进一步加强对这些漏洞的防御,我们试图绕过它的防御,并成功地窃取(或提取)信息。具体来说,我们从两个不同的方向分析了Nasty Teacher,然后仔细地利用它们来开发简单而有效的方法,称为HTC和SCM,这将从Nasty Teacher中获得的学习在标准数据集上提高了68.63%。此外,我们还探索了一种基于我们对偷窃的见解的临时防御方法。我们在不同模型/设置上的详细实验和消融证明了我们方法的有效性。
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Distilling the Undistillable: Learning from a Nasty Teacher
The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work Nasty Teacher proposed to develop teachers which can not be distilled or imitated by models attacking it. However, the promise of confidentiality offered by a nasty teacher is not well studied, and as a further step to strengthen against such loopholes, we attempt to bypass its defense and steal (or extract) information in its presence successfully. Specifically, we analyze Nasty Teacher from two different directions and subsequently leverage them carefully to develop simple yet efficient methodologies, named as HTC and SCM, which increase the learning from Nasty Teacher by upto 68.63% on standard datasets. Additionally, we also explore an improvised defense method based on our insights of stealing. Our detailed set of experiments and ablations on diverse models/settings demonstrate the efficacy of our approach.
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