Alberto Bosio

Hybrid Hardening for Robust DNNs Under Adversarial Attacks

Deep Neural Networks are vulnerable to adversarial perturbations, which can cause failures in safety-critical systems. State-of-the-art countermeasures often incur prohibitive overheads, creating a need for cost-efficient robustness strategies. This paper proposes a hybrid hardening technique integrating Triple Modular Redundancy with Adversarial Training, Label Smoothing, and Iterative Pruning to reduce hardening cost while preserving accuracy. Experimental results demonstrate that our approach maintains up to 86.7% accuracy under strong adversarial perturbations (ϵ = 0.1), outperforming unprotected models by a factor of 4.8x while reducing memory footprint up to 76% through pruning.