Toward a Generalized Defense Across Sparse, Continuous, and Structured Parameter Attacks

Summary

The authors present ParDef, a defense for deep neural networks against diverse parameter attacks, which directly tamper with model weights and persist across inferences. ParDef combines keyed channel reparameterization, QC-LDPC quantization with error correction, and adaptive robust inference. Evaluated on CIFAR-10, CIFAR-100, and Tiny-ImageNet with ResNet and VGG models, plus DeiT on ImageNet-1K and CIFAR-100, it consistently reduces attack success rates while keeping high model performance and moderate deployment overhead.

Mitigation

ParDef: keyed channel reparameterization, QC-LDPC quantization, and adaptive robust inference, applied to securing at-rest model parameters in DNN deployments.

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