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 amplifying model parameters. In particular, we
provide theoretical analysis to safeguard the foundations of the PSC
phenomenon. We also design an adaptive method to select BN layers to scale up
for effective detection. Extensive experiments are conducted on benchmark
datasets, verifying the effectiveness and efficiency of our IBD-PSC method and
its resistance to adaptive attacks.

这篇论文提出了一种名为 IBD-PSC 的简单而有效的输入层背门检测方法，通过放大模型参数，利用参数相关的一致性现象来过滤恶意测试图像，并设计了一种自适应方法选择 BN 层进行有效检测，通过基准数据集上的大量实验证实了 IBD-PSC 方法的有效性和效率，以及其对自适应攻击的抵抗性。