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On the Limitations of Denoising Strategies as Adversarial Defenses
Zhonghan Niu, Zhaoxi Chen, Linyi Li +3
As adversarial attacks against machine learning models have raised increasing concerns, many denoising-based defense approaches have been proposed. In this paper, we summarize and…
With False Friends Like These, Who Can Notice Mistakes?
Lue Tao, Lei Feng, Jinfeng Yi +1
Adversarial examples crafted by an explicit adversary have attracted significant attention in machine learning. However, the security risk posed by a potential false friend has bee…
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Tianxin Wei, Fuli Feng, Jiawei Chen +3
The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e.…
Provably Robust Metric Learning
Lu Wang, Xuanqing Liu, Jinfeng Yi +2
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less…
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
Lu Wang, Huan Zhang, Jinfeng Yi +2
Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of p…