25 citations · 40 across the 10 of their papers we have counts for
10 papers
Adapting to Evolving Adversaries with Regularized Continual Robust Training
Sihui Dai, Christian Cianfarani, Arjun Bhagoji +2
Robust training methods typically defend against specific attack types, such as Lp attacks with fixed budgets, and rarely account for the fact that defenders may encounter new atta…
Position: Towards Resilience Against Adversarial Examples
Sihui Dai, Chong Xiang, Tong Wu +1
Current research on defending against adversarial examples focuses primarily on achieving robustness against a single attack type such as or -bounded attack…
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
Chong Xiang, Tong Wu, Sihui Dai +3
State-of-the-art defenses against adversarial patch attacks can now achieve strong certifiable robustness with a marginal drop in model utility. However, this impressive performanc…
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Sihui Dai, Saeed Mahloujifar, Chong Xiang +3
The bulk of existing research in defending against adversarial examples focuses on defending against a single (typically bounded Lp-norm) attack, but for a practical setting, machi…
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji +4
Finding classifiers robust to adversarial examples is critical for their safe deployment. Determining the robustness of the best possible classifier under a given threat model for…
Formulating Robustness Against Unforeseen Attacks
Sihui Dai, Saeed Mahloujifar, Prateek Mittal
Existing defenses against adversarial examples such as adversarial training typically assume that the adversary will conform to a specific or known threat model, such as p…
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai, Saeed Mahloujifar, Prateek Mittal
Deep neural networks are known to be vulnerable to adversarially perturbed inputs. A commonly used defense is adversarial training, whose performance is influenced by model capacit…
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina +4
While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-w…
Neural Networks with Recurrent Generative Feedback
Yujia Huang, James Gornet, Sihui Dai +4
Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The B…
Out-of-Distribution Detection Using Neural Rendering Generative Models
Yujia Huang, Sihui Dai, Tan Nguyen +2
Out-of-distribution (OoD) detection is a natural downstream task for deep generative models, due to their ability to learn the input probability distribution. There are mainly two…