6 citations · 12 across the 5 of their papers we have counts for
5 papers
A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation
Zhiyi Xue, Si Liu, Zhaodi Zhang +2
The robustness of deep neural networks (DNNs) is crucial to the hosting system's reliability and security. Formal verification has been demonstrated to be effective in providing pr…
Boosting Verified Training for Robust Image Classifications via Abstraction
Zhaodi Zhang, Zhiyi Xue, Yang Chen +4
This paper proposes a novel, abstraction-based, certified training method for robust image classifiers. Via abstraction, all perturbed images are mapped into intervals before feedi…
DualApp: Tight Over-Approximation for Neural Network Robustness Verification via Under-Approximation
Yiting Wu, Zhaodi Zhang, Zhiyi Xue +2
The robustness of neural networks is fundamental to the hosting system's reliability and security. Formal verification has been proven to be effective in providing provable robustn…
Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks
Zhaodi Zhang, Yiting Wu, Si Liu +2
The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of ap…
Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels
Wenjie Wan, Zhaodi Zhang, Yiwei Zhu +2
Deep Neural Networks (DNNs) have become key components of many safety-critical applications such as autonomous driving and medical diagnosis. However, DNNs have been shown sufferin…