{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neuro-symbolic-verification-of-deep-neural","title":"Neuro-Symbolic Verification of Deep Neural Networks","arxiv_id":"2203.00938","date":"2022-03-02","proceeding":null,"authors":["Xuan Xie","Kristian Kersting","Daniel Neider"],"abstract":"Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over the inputs and output of a network. While adversarial robustness and fairness fall under this category, many real-world properties (e.g., \"an autonomous vehicle has to stop in front of a stop sign\") remain outside the scope of existing verification technology. To mitigate this severe practical restriction, we introduce a novel framework for verifying neural networks, named neuro-symbolic verification. The key idea is to use neural networks as part of the otherwise logical specification, enabling the verification of a wide variety of complex, real-world properties, including the one above. Moreover, we demonstrate how neuro-symbolic verification can be implemented on top of existing verification infrastructure for neural networks, making our framework easily accessible to researchers and practitioners alike.","url_abs":"https://arxiv.org/abs/2203.00938v1","url_pdf":"https://arxiv.org/pdf/2203.00938v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neuro-symbolic-verification-of-deep-neural","repo_url":"https://github.com/lebronx/neuro-symbolic-verification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.00938","atlas_url":"https://app.syntology.ai/?focus=2203.00938","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}