{"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/taps-connecting-certified-and-adversarial","title":"TAPS: Connecting Certified and Adversarial Training","arxiv_id":"2305.04574","date":"2023-05-08","proceeding":null,"authors":["Yuhao Mao","Mark Niklas Müller","Marc Fischer","Martin Vechev"],"abstract":"Training certifiably robust neural networks remains a notoriously hard problem. On one side, adversarial training optimizes under-approximations of the worst-case loss, which leads to insufficient regularization for certification, while on the other, sound certified training methods optimize loose over-approximations, leading to over-regularization and poor (standard) accuracy. In this work we propose TAPS, an (unsound) certified training method that combines IBP and PGD training to yield precise, although not necessarily sound, worst-case loss approximations, reducing over-regularization and increasing certified and standard accuracies. Empirically, TAPS achieves a new state-of-the-art in many settings, e.g., reaching a certified accuracy of $22\\%$ on TinyImageNet for $\\ell_\\infty$-perturbations with radius $\\epsilon=1/255$. We make our implementation and networks public at https://github.com/eth-sri/taps.","url_abs":"https://arxiv.org/abs/2305.04574v2","url_pdf":"https://arxiv.org/pdf/2305.04574v2.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":"taps-connecting-certified-and-adversarial","repo_url":"https://github.com/eth-sri/taps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"taps-connecting-certified-and-adversarial","repo_url":"https://github.com/eth-sri/ibp-propagation-tightness","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.04574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04574"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eth-sri/taps","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eth-sri/ibp-propagation-tightness","reach":{"status":"ok"}}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"bd05364d124320c9","entry":"project_to_bounds","repo":"eth-sri/taps","repo_kind":"official","path":"torch_model_wrapper.py","file_url":"https://github.com/eth-sri/taps/blob/HEAD/torch_model_wrapper.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bd05364d124320c9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}