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IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
May 17, 2024, 4:41 a.m. | Linshan Hou, Ruili Feng, Zhongyun Hua, Wei Luo, Leo Yu Zhang, Yiming Li
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a 'firewall' to filter out malicious testing images. Our method is motivated by an intriguing phenomenon, i.e., parameter-oriented scaling consistency (PSC), where the prediction confidences of poisoned samples are significantly more consistent than those of benign ones when …
abstract arxiv attacks backdoor cs.cr cs.lg detection filter firewall hidden images networks neural networks paper scaling simple testing training type via vulnerable
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