2022/05/23 by Jarkko Peltomäki, Peltomäki, Jarkko, Iván Porres +1
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Physical Unclonable Functions (PUFs) and Hardware Security #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2205.11057
openalex publication_date 2022/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We consider the problem of falsifying safety requirements of Cyber-Physical Systems expressed in signal temporal logic (STL). This problem can be turned into an optimization problem via STL robustness functions. In this paper, our focus is in falsifying systems with multiple requirements. We propose to solve such conjunctive requirements using online generative adversarial networks (GANs) as test generators. Our main contribution is an algorithm which falsifies a conjunctive requirement φ1 ∧ ⋯ ∧ φn by using a GAN for each requirement φi separately. Using ideas from multi-armed bandit algorithms, our algorithm only trains a single GAN at every step, which saves resources. Our experiments indicate that, in addition to saving resources, this multi-armed bandit algorithm can falsify requirements with fewer number of executions on the system under test when compared to (i) an algorithm training a single GAN for the complete conjunctive requirement and (ii) an algorithm always training n GANs at each step.