158 citations · 212 across the 8 of their papers we have counts for
5 papers · 1 filter
Fastened CROWN: Tightened Neural Network Robustness Certificates
Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong +3
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reli…
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Jeet Mohapatra, Tsui-Wei, Weng +3
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the -norm t…
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
Yuh-Shyang Wang, Tsui-Wei Weng, Luca Daniel
Deep neural networks are known to be fragile to small adversarial perturbations. This issue becomes more critical when a neural network is interconnected with a physical system in…
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng +3
The vulnerability to adversarial attacks has been a critical issue for deep neural networks. Addressing this issue requires a reliable way to evaluate the robustness of a network.…
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen +3
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important…