Improving Adversarial Robustness in Weight-quantized Neural Networks

29 December 2020

Papers citing "Improving Adversarial Robustness in Weight-quantized Neural Networks"

6 / 6 papers shown

Title
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs Amira Guesmi B. Ouni Muhammad Shafique MQ AAML 36 0 0 10 Mar 2025
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks Ferheen Ayaz Idris Zakariyya José Cano S. Keoh Jeremy Singer D. Pau Mounia Kharbouche-Harrari 19 5 0 25 Apr 2023
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks Huu Le R. Høier Che-Tsung Lin Christopher Zach 55 17 0 06 Dec 2021
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency Yonggan Fu Yang Katie Zhao Qixuan Yu Chaojian Li Yingyan Lin AAML 52 12 0 11 Sep 2021
On the Adversarial Robustness of Quantized Neural Networks Micah Gorsline James T. Smith Cory E. Merkel AAML 54 18 0 01 May 2021
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016