26 citations · 106 across the 13 of their papers we have counts for
16 papers
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Yizheng Chen, Zhoujie Ding, Lamya Alowain +2
We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes fro…
Continuous Learning for Android Malware Detection
Yizheng Chen, Zhoujie Ding, David Wagner
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and in…
REAP: A Large-Scale Realistic Adversarial Patch Benchmark
Nabeel Hingun, Chawin Sitawarin, Jerry Li +1
Machine learning models are known to be susceptible to adversarial perturbation. One famous attack is the adversarial patch, a sticker with a particularly crafted pattern that make…
Part-Based Models Improve Adversarial Robustness
Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen +2
We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks by introducing a part-based model for object classification…
Demystifying the Adversarial Robustness of Random Transformation Defenses
Chawin Sitawarin, Zachary Golan-Strieb, David Wagner
Neural networks' lack of robustness against attacks raises concerns in security-sensitive settings such as autonomous vehicles. While many countermeasures may look promising, only…
SLIP: Self-supervision meets Language-Image Pre-training
Norman Mu, Alexander Kirillov, David Wagner +1
Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach to le…