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arXiv Computer Vision By Yi Yang, Jinyang Huang, Binbin Liu, Feng-Qi Cui, Xiaokang Zhou, Haiming Jin, Zhi Liu, Jie Zhang, Meng Li

Checkerboard: Closed-Form and Data-Independent Trigger Design for Clean-Label Backdoor Attacks

Read the original on arXiv Computer Vision →

The paper introduces Checkerboard, a clean‑label backdoor attack that uses a closed‑form, data‑independent trigger design based on an input‑space Fisher‑separability objective and a ridge four‑neighbor local‑smoothness prior. This approach yields a pixel‑wise checkerboard trigger without requiring data access, surrogate model training, or iterative optimization, and it outperforms existing norm‑bounded clean‑label attacks across four benchmark datasets. On CIFAR‑10, poisoning 20 samples with a 10/255 perturbation achieves a 95.72% attack success rate, while on IN‑100 a 0.4% global poisoning rate yields over 83% ASR without harming clean accuracy, and the attack remains robust against state‑of‑the‑art defenses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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