153 citations · 224 across the 15 of their papers we have counts for
7 papers · 1 filter
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
Xuran Li, Jingyi Wang, Xiaohan Yuan +1
It is often desirable to remove (a.k.a. unlearn) a specific part of the training data from a trained neural network model. A typical application scenario is to protect the data hol…
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
Yao Lu, Peixin Zhang, Jingyi Wang +3
Deep learning has revolutionized computing in many real-world applications, arguably due to its remarkable performance and extreme convenience as an end-to-end solution. However, d…
Towards Certified Probabilistic Robustness with High Accuracy
Ruihan Zhang, Peixin Zhang, Jun Sun
Adversarial examples pose a security threat to many critical systems built on neural networks (such as face recognition systems, and self-driving cars). While many methods have bee…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Yizhen Dong, Peixin Zhang, Jingyi Wang +7
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs…
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang, Guoliang Dong, Jun Sun +2
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a nor…
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Jingyi Wang, Jun Sun, Peixin Zhang +1
Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples. Adversarial samples are often crafted through adversarial perturbati…