Titlebook: Computer Aided Verification; 34th International C Sharon Shoham,Yakir Vizel Conference proceedings‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if applicabl

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Handling of Multidimensional Pareto Curves,Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on

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Fast and Scalable Run-time Scheduling,ustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This . process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components (., deep

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https://doi.org/10.1007/978-1-4020-6344-2lness is hampered by their susceptibility to .. Recently, many methods for measuring and improving a network’s robustness to adversarial perturbations have been proposed, and this growing body of research has given rise to numerous explicit or implicit notions of robustness. Connections between thes

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Conclusions and future research work,f PDR to be an ingenious combination of verification and refutation attempts based on the Knaster–Tarski and Kleene theorems. We introduce four concrete instances of LT-PDR, derive their implementation from a generic Haskell implementation of LT-PDR, and experimentally evaluate them. We also present

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Factor Analysis in a Mixed-Methods Contextsuch a long history, BMC still faces scalability challenges as programs continue to grow larger and more complex. One approach that has proven to be effective in verifying large programs is called Counterexample Guided Abstraction Refinement (CEGAR). In this work, we propose a complementary approach