{"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/attentive-filtering-networks-for-audio-replay","title":"Attentive Filtering Networks for Audio Replay Attack Detection","arxiv_id":"1810.13048","date":"2018-10-31","proceeding":null,"authors":["Cheng-I Lai","Alberto Abad","Korin Richmond","Junichi Yamagishi","Najim Dehak","Simon King"],"abstract":"An attacker may use a variety of techniques to fool an automatic speaker\nverification system into accepting them as a genuine user. Anti-spoofing\nmethods meanwhile aim to make the system robust against such attacks. The\nASVspoof 2017 Challenge focused specifically on replay attacks, with the\nintention of measuring the limits of replay attack detection as well as\ndeveloping countermeasures against them. In this work, we propose our replay\nattacks detection system - Attentive Filtering Network, which is composed of an\nattention-based filtering mechanism that enhances feature representations in\nboth the frequency and time domains, and a ResNet-based classifier. We show\nthat the network enables us to visualize the automatically acquired feature\nrepresentations that are helpful for spoofing detection. Attentive Filtering\nNetwork attains an evaluation EER of 8.99$\\%$ on the ASVspoof 2017 Version 2.0\ndataset. With system fusion, our best system further obtains a 30$\\%$ relative\nimprovement over the ASVspoof 2017 enhanced baseline system.","url_abs":"http://arxiv.org/abs/1810.13048v1","url_pdf":"http://arxiv.org/pdf/1810.13048v1.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":"attentive-filtering-networks-for-audio-replay","repo_url":"https://github.com/jefflai108/Attentive-Filtering-Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"speaker-verification","task_name":"Speaker Verification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.13048","atlas_url":"https://app.syntology.ai/?focus=1810.13048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.13048"}},"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/jefflai108/Attentive-Filtering-Network","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"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":"862f43e303f0eb09","entry":"compute_confuse","repo":"jefflai108/Attentive-Filtering-Network","repo_kind":"official","path":"src/v1_metrics.py","file_url":"https://github.com/jefflai108/Attentive-Filtering-Network/blob/HEAD/src/v1_metrics.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":"862f43e303f0eb09"}},{"code_sha256_prefix":"9a44e9b62ee06507","entry":"compute_eer","repo":"jefflai108/Attentive-Filtering-Network","repo_kind":"official","path":"src/v1_metrics.py","file_url":"https://github.com/jefflai108/Attentive-Filtering-Network/blob/HEAD/src/v1_metrics.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":"9a44e9b62ee06507"}},{"code_sha256_prefix":"b4d324af8187acc6","entry":"setup_logs","repo":"jefflai108/Attentive-Filtering-Network","repo_kind":"official","path":"src/v1_logger.py","file_url":"https://github.com/jefflai108/Attentive-Filtering-Network/blob/HEAD/src/v1_logger.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":"b4d324af8187acc6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}