Back in Black: A Comparative Evaluation of Recent State-Of-The-Art
Black-Box Attacks

Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks

29 September 2021

Ethan Rathbun

Marten van Dijk

Papers citing "Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks"

6 / 6 papers shown

Title
On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing Tao Bai Xingjian Tian Yonghao Xu B. Wen AAML 41 0 0 20 Jan 2025
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit Duanyi Yao Songze Li Ye Xue Jin Liu FedML AAML 27 1 0 08 Aug 2024
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents Ethan Rathbun Christopher Amato Alina Oprea OffRL AAML 43 3 0 30 May 2024
Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks Roey Bokobza Yisroel Mirsky AAML 30 0 0 14 Mar 2024
On the Robustness of AlphaFold: A COVID-19 Case Study Ismail R. Alkhouri Sumit Kumar Jha Andre Beckus George K. Atia Alvaro Velasquez Rickard Ewetz Arvind Ramanathan Susmit Jha AAML 20 3 0 10 Jan 2023
Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning Ren Wang Zhe Xu Alfred Hero 22 0 0 06 Oct 2021