RESQ: A Unified Framework for REliability- and Security Enhancement of Quantized Deep Neural Networks

ArXi:2603.15413v1 Announce Type: cross This work proposes a unified three-stage framework that produces a quantized DNN with balanced fault and attack robustness. The first stage improves attack resilience via fine-tuning that desensitizes feature representations to small input perturbations. The second stage reinforces fault resilience through fault-aware fine-tuning under simulated bit-flip faults. Finally, a lightweight post-