L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks

1 code implementation • 27 Jan 2024 • Ping Guo, Fei Liu, Xi Lin, Qingchuan Zhao, Qingfu Zhang

In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security.

Adversarial Attack Computational Efficiency +2

PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks

no code implementations • 19 Jan 2024 • Ping Guo, Zhiyuan Yang, Xi Lin, Qingchuan Zhao, Qingfu Zhang

Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters.

AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection

1 code implementation • 14 Dec 2023 • Xiangtao Meng, Li Wang, Shanqing Guo, Lei Ju, Qingchuan Zhao

While DeepFake applications are becoming popular in recent years, their abuses pose a serious privacy threat.

Adversarial Attack Attribute +2