CSC: Turning the Adversary's Poison against Itself
Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to misclassify triggered inputs as adversary-specified labels while maintaining performance on clean data. Existing poison restraint-based defenses often suffer from inadequate detection against specific attack variants and compromise model utility through unlearning methods that lead to accuracy degradation. This paper conducts a comprehensive analysis of backdoor attack dynamics during model training, revealing that poisoned samples form isolated clusters in latent space early on, with triggers acting as dominant features distinct from benign ones. Lev...
| Authors | Yuchen Shi, Xin Guo, Huajie Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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