Papers › Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
Zhezhi He, Adnan Siraj Rakin, Jingtao Li, Chaitali Chakrabarti, Deliang Fan
Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit-Flip Attack (BFA). BFA has shown extraordinary attacking ability, where the adversary can malfunction a quantized Deep Neural Network (DNN) as a random guess, through malicious bit-flips on a small set of vulnerable weight bits (e.g., 13 out of 93 millions bits of 8-bit quantized ResNet-18). However, there are no effective defensive methods to enhance the fault-tolerance capability of DNN against such BFA. In this work, we conduct comprehensive investigations on BFA and propose to leverage binarization-aware training and its relaxation -- piece-wise clustering as simple and effective countermeasures to BFA. The experiments show that, for BFA to achieve the identical prediction accuracy degradation (e.g., below 11% on CIFAR-10), it requires 19.3x and 480.1x more effective malicious bit-flips on ResNet-20 and VGG-11 respectively, compared to defend-free counterparts.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections