Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning

29 November 2022

Papers citing "Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning"

6 / 6 papers shown

Title
Impact of Architectural Modifications on Deep Learning Adversarial Robustness Firuz Juraev Mohammed Abuhamad Simon S. Woo George K Thiruvathukal Tamer Abuhmed AAML 30 0 0 03 May 2024
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed AAML 14 2 0 21 Jul 2023
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed AAML 19 1 0 13 Jul 2023
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems Eldor Abdukhamidov Mohammed Abuhamad George K. Thiruvathukal Hyoungshick Kim Tamer Abuhmed AAML 25 2 0 12 Jul 2023
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 252 36,362 0 25 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,835 0 08 Jul 2016