Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective

Published: 16 Jan 2024, Last Modified: 05 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Backdoor, Trigger, Dataset Condensation, Dataset Distillation
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TL;DR: We theoretically analyze the backdoor attack using dataset condensation and then propose a trigger pattern generation algorithm.
Abstract: Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks.
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Primary Area: societal considerations including fairness, safety, privacy
Submission Number: 6151