Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks

27 September 2019

Papers citing "Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks"

6 / 6 papers shown

Title
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Amira Guesmi B. Ouni Muhammad Shafique AAML 74 0 0 22 Nov 2024
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence Benoît Coqueret Mathieu Carbone Olivier Sentieys Gabriel Zaid 45 2 0 23 Nov 2023
Uncovering the Hidden Cost of Model Compression Diganta Misra Muawiz Chaudhary Agam Goyal Bharat Runwal Pin-Yu Chen VLM 28 0 0 29 Aug 2023
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
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 258 3,109 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 263 5,833 0 08 Jul 2016