Adversarial Example
Adversarial examples are subtly altered inputs designed to fool machine learning models, primarily deep neural networks (DNNs), into making incorrect predictions. Current research focuses on improving model robustness against these attacks, exploring techniques like ensemble methods, multi-objective representation learning, and adversarial training, often applied to architectures such as ResNets and Vision Transformers. Understanding and mitigating the threat of adversarial examples is crucial for ensuring the reliability and security of AI systems across diverse applications, from image classification and natural language processing to malware detection and autonomous driving. The development of robust defenses and effective attack detection methods remains a significant area of ongoing investigation.
Papers
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao
Searching for the Essence of Adversarial Perturbations
Dennis Y. Menn, Tzu-hsun Feng, Hung-yi Lee
Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to Strengthen Robustness of Android Malware Detection
Hamid Bostani, Zhengyu Zhao, Zhuoran Liu, Veelasha Moonsamy
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
Songlin Yang, Wei Wang, Chenye Xu, Ziwen He, Bo Peng, Jing Dong
Certified Robustness Against Natural Language Attacks by Causal Intervention
Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang
Defending a Music Recommender Against Hubness-Based Adversarial Attacks
Katharina Hoedt, Arthur Flexer, Gerhard Widmer
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization
Shudong Zhang, Haichang Gao, Tianwei Zhang, Yunyi Zhou, Zihui Wu