Seminar: Formal Verification and Testing of Deep Neural Networks
- Seminar (Prof. Dr. Stefan Leue)
Monday 15:15 - 16:45 Room: PZ 901 Start: 15 April 2019
- May 6: Topic 5; May 13: Topic 11; May 27: Topic 6; June 3: Topic 1; June 17: Topic 2; July 8: Topic 7.
- No seminar on May 20, June 10 (Holiday), June 24, July 1.
The use of machine learning techniques, such as Deep Neural Networks (DNNs), have a significant impact on classical system design processes. The specification of their desired capabilities is often vague or incomplete. Since they do not possess an internal structure that provides obvious explanations of their behavior it is difficult to perform structural verification or testing. This seminar aims at presenting some first approaches towards the development of formal verification technology in order to proof properties of DNNs, or to test whether DNNs satisfy certain properties.
This seminar aims at presenting some first approaches towards the development of formal verification technology in order to proof properties of DNNs, or to test whether DNNs satisfy certain properties.
Expected course achievement
30 - 45 min. presentation + 15 min. discussion
120 hours, of which 28 hours are spent in class and 92 hours of self study.
Master-level course. Open to Bachelor-level students.
The course will be taught in English. All course materials will be in English.
Topic 1 (taken): Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer:
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks. CAV (1) 2017: 97-117
Topic 2: Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Efficient Formal Safety Analysis of Neural Networks.
NeurIPS 2018: 6369-6379
Topic 3: Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Formal Security Analysis of Neural Networks using Symbolic Intervals.
USENIX Security Symposium 2018: 1599-1614
Topic 4: Osbert Bastani, Yewen Pu, Armando Solar-Lezama:
Verifiable Reinforcement Learning via Policy Extraction.
NeurIPS 2018: 2499-2509
Topic 5 (taken): A. Sharma, H. Wehrheim:
Testing Machine Learning Algorithms for Balanced Data Usage
IEEE International Conference on Software Testing, Verification and Validation (ICST), Xi’an, China, 2019.
Topic 6 (taken): Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana:
DeepXplore: Automated Whitebox Testing of Deep Learning Systems.
SOSP 2017: 1-18
Topic 7: Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray:
DeepTest: automated testing of deep-neural-network-driven autonomous cars.
ICSE 2018: 303-314
Topic 8: Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin T. Vechev:
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation.
IEEE Symposium on Security and Privacy 2018: 3-18
Topic 9: Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
An abstract domain for certifying neural networks.
PACMPL 3(POPL): 41:1-41:30 (2019)
Topic 10: Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening:
Concolic testing for deep neural networks.
ASE 2018: 109-119
Topic 11 (taken): John Törnblom, Simin Nadjm-Tehrani:
Formal Verification of Random Forests in Safety-Critical Applications.
FTSCS 2018: 55-71
Topic 12: Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, Ashish Tiwari:
Output Range Analysis for Deep Feedforward Neural Networks.
NFM 2018: 121-138