Safety Verification of Deep Neural Networks

2017/01/01 by Xiaowei Huang, Marta Kwiatkowska, Sen Wang +1 · 12 citations
Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Physical Unclonable Functions (PUFs) and Hardware Security

paper · doi:10.1007/978-3-319-63387-9_1

crossref issued 2017/01/01 · crossref published 2017/01/01 · crossref published-print 2017/01/01 · openalex publication_date 2017/01/01 · crossref created 2017/07/12 · crossref published-online 2017/07/13 · crossref deposited 2024/06/25 · openalex created_date 2025/10/10 · crossref indexed 2026/08/02 · openalex updated_date 2026/08/04

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Understanding Deep Convolutional Networks
• End to End Learning for Self-Driving Cars
• Towards Verified Artificial Intelligence
• Attentive Explanations: Justifying Decisions and Pointing to the Evidence
• Safety Verification of Deep Neural Networks
• Adversarial Examples in the Physical World

Cited by

• Input Validation for Neural Networks via Runtime Local Robustness Verification
• Robustness Guarantees for Deep Neural Networks on Videos
• CRNs Exposed: Systematic Exploration of Chemical Reaction Networks
• Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
• On applying residual reasoning within neural network verification
• Probabilistic Verification of Fairness Properties via Concentration
• Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities
• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
• Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
• Efficient Certified Defenses Against Patch Attacks on Image Classifiers
• Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
• Marta Kwiatkowska [wikipedia]