{"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/asvspoof-2021-towards-spoofed-and-deepfake","title":"ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild","arxiv_id":"2210.02437","date":"2022-10-05","proceeding":null,"authors":["Xuechen Liu","Xin Wang","Md Sahidullah","Jose Patino","Héctor Delgado","Tomi Kinnunen","Massimiliano Todisco","Junichi Yamagishi","Nicholas Evans","Andreas Nautsch","Kong Aik Lee"],"abstract":"Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and challenge series, has followed the same trend. This article provides a summary of the ASVspoof 2021 challenge and the results of 54 participating teams that submitted to the evaluation phase. For the logical access (LA) task, results indicate that countermeasures are robust to newly introduced encoding and transmission effects. Results for the physical access (PA) task indicate the potential to detect replay attacks in real, as opposed to simulated physical spaces, but a lack of robustness to variations between simulated and real acoustic environments. The Deepfake (DF) task, new to the 2021 edition, targets solutions to the detection of manipulated, compressed speech data posted online. While detection solutions offer some resilience to compression effects, they lack generalization across different source datasets. In addition to a summary of the top-performing systems for each task, new analyses of influential data factors and results for hidden data subsets, the article includes a review of post-challenge results, an outline of the principal challenge limitations and a road-map for the future of ASVspoof.","url_abs":"https://arxiv.org/abs/2210.02437v3","url_pdf":"https://arxiv.org/pdf/2210.02437v3.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"asvspoof-2021-towards-spoofed-and-deepfake","repo_url":"https://github.com/asvspoof-challenge/2021","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2210.02437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02437"}},"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/asvspoof-challenge/2021","reach":null}],"summary":{"ran_fixture":2,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":"3a889d3debbcead8","entry":"compute_det_curve","repo":"asvspoof-challenge/2021","repo_kind":"official","path":"eval-package/eval_metrics.py","file_url":"https://github.com/asvspoof-challenge/2021/blob/HEAD/eval-package/eval_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3a889d3debbcead8"}},{"code_sha256_prefix":"047cd2d292cdc66d","entry":"compute_eer","repo":"asvspoof-challenge/2021","repo_kind":"official","path":"eval-package/eval_metrics.py","file_url":"https://github.com/asvspoof-challenge/2021/blob/HEAD/eval-package/eval_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"047cd2d292cdc66d"}},{"code_sha256_prefix":"23775bf0e385bd4c","entry":"obtain_asv_error_rates","repo":"asvspoof-challenge/2021","repo_kind":"official","path":"eval-package/eval_metrics.py","file_url":"https://github.com/asvspoof-challenge/2021/blob/HEAD/eval-package/eval_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"23775bf0e385bd4c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}