{"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/freeeagle-detecting-complex-neural-trojans-in","title":"FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases","arxiv_id":"2302.14500","date":"2023-02-28","proceeding":null,"authors":["Chong Fu","Xuhong Zhang","Shouling Ji","Ting Wang","Peng Lin","Yanghe Feng","Jianwei Yin"],"abstract":"Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs. However, if the input contains a particular trigger, the trojaned model will have attacker-chosen abnormal behavior. Although many backdoor detection methods exist, most of them assume that the defender has access to a set of clean validation samples or samples with the trigger, which may not hold in some crucial real-world cases, e.g., the case where the defender is the maintainer of model-sharing platforms. Thus, in this paper, we propose FreeEagle, the first data-free backdoor detection method that can effectively detect complex backdoor attacks on deep neural networks, without relying on the access to any clean samples or samples with the trigger. The evaluation results on diverse datasets and model architectures show that FreeEagle is effective against various complex backdoor attacks, even outperforming some state-of-the-art non-data-free backdoor detection methods.","url_abs":"https://arxiv.org/abs/2302.14500v1","url_pdf":"https://arxiv.org/pdf/2302.14500v1.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":"freeeagle-detecting-complex-neural-trojans-in","repo_url":"https://github.com/FuChong-cyber/Data-Free-Neural-Backdoor-Detector-FreeEagle","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"backdoor-attack","task_name":"Backdoor Attack"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.14500","atlas_url":"https://app.syntology.ai/?focus=2302.14500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14500"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/FuChong-cyber/Data-Free-Neural-Backdoor-Detector-FreeEagle","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"9574bb04e97465bd","entry":"save_to_df","repo":"FuChong-cyber/Data-Free-Neural-Backdoor-Detector-FreeEagle","repo_kind":"official","path":"MLBackdoorDetection/inspect_multiple_models.py","file_url":"https://github.com/FuChong-cyber/Data-Free-Neural-Backdoor-Detector-FreeEagle/blob/HEAD/MLBackdoorDetection/inspect_multiple_models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9574bb04e97465bd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}