{"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/geometric-robustness-of-deep-networks","title":"Geometric robustness of deep networks: analysis and improvement","arxiv_id":"1711.09115","date":"2017-11-24","proceeding":"CVPR 2018 6","authors":["Can Kanbak","Seyed-Mohsen Moosavi-Dezfooli","Pascal Frossard"],"abstract":"Deep convolutional neural networks have been shown to be vulnerable to\narbitrary geometric transformations. However, there is no systematic method to\nmeasure the invariance properties of deep networks to such transformations. We\npropose ManiFool as a simple yet scalable algorithm to measure the invariance\nof deep networks. In particular, our algorithm measures the robustness of deep\nnetworks to geometric transformations in a worst-case regime as they can be\nproblematic for sensitive applications. Our extensive experimental results show\nthat ManiFool can be used to measure the invariance of fairly complex networks\non high dimensional datasets and these values can be used for analyzing the\nreasons for it. Furthermore, we build on Manifool to propose a new adversarial\ntraining scheme and we show its effectiveness on improving the invariance\nproperties of deep neural networks.","url_abs":"http://arxiv.org/abs/1711.09115v1","url_pdf":"http://arxiv.org/pdf/1711.09115v1.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":"geometric-robustness-of-deep-networks","repo_url":"https://github.com/moosavism/ManiFool","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.09115"}},"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/moosavism/ManiFool","reach":null}],"summary":{"ran_honours":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"74b039ed6f74306c","entry":"get_output_label","repo":"moosavism/ManiFool","repo_kind":"official","path":"functions/algorithms/manifool.py","file_url":"https://github.com/moosavism/ManiFool/blob/HEAD/functions/algorithms/manifool.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"74b039ed6f74306c"}},{"code_sha256_prefix":"607f85ca0f548ce7","entry":"tform_2_tau","repo":"moosavism/ManiFool","repo_kind":"official","path":"functions/helpers/geodesic_distance.py","file_url":"https://github.com/moosavism/ManiFool/blob/HEAD/functions/helpers/geodesic_distance.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"607f85ca0f548ce7"}},{"code_sha256_prefix":"4da985ce07579a33","entry":"geodesic_distance","repo":"moosavism/ManiFool","repo_kind":"official","path":"functions/helpers/geodesic_distance.py","file_url":"https://github.com/moosavism/ManiFool/blob/HEAD/functions/helpers/geodesic_distance.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4da985ce07579a33"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}