4 papers · 1 filter
Toward Patch Robustness Certification and Detection for Deep Learning Systems Beyond Consistent Samples
Qilin Zhou, Zhengyuan Wei, Haipeng Wang +2
Patch robustness certification is an emerging kind of provable defense technique against adversarial patch attacks for deep learning systems. Certified detection ensures the detect…
A3Rank: Augmentation Alignment Analysis for Prioritizing Overconfident Failing Samples for Deep Learning Models
Zhengyuan Wei, Haipeng Wang, Qilin Zhou +1
Sharpening deep learning models by training them with examples close to the decision boundary is a well-known best practice. Nonetheless, these models are still error-prone in prod…
Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
Haipeng Wang, Zhengyuan Wei, Qilin Zhou +1
In the testing-retraining pipeline for enhancing the robustness property of deep learning (DL) models, many state-of-the-art robustness-oriented fuzzing techniques are metric-orien…
CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models
Qilin Zhou, Zhengyuan Wei, Haipeng Wang +2
Patch robustness certification is an emerging kind of defense technique against adversarial patch attacks with provable guarantees. There are two research lines: certified recovery…