{"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":"/code/compute-eer","entry":"compute_eer","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":12,"n_papers_ran":9,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":9,"n_samples_ran":6,"n_samples_fingerprinted":6,"n_places":12,"n_places_pointer_only":5,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":3,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2409.13382","paper":"/paper/audio-codec-augmentation-for-robust","title":"Audio Codec Augmentation for Robust Collaborative Watermarking of Speech Synthesis","date":"2024-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ljuvela/collaborative-watermarking-with-codecs","path":"src/collaborative_watermarking/metrics.py","file_url":"https://github.com/ljuvela/collaborative-watermarking-with-codecs/blob/HEAD/src/collaborative_watermarking/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6c660ac679abd4e2","mcp_get_code":{"code_sha256":"6c660ac679abd4e2"}},{"arxiv_id":"2408.16132","paper":"/paper/svdd-2024-the-inaugural-singing-voice","title":"SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge","date":"2024-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"svddchallenge/ctrsvdd2024_baseline","path":"utils.py","file_url":"https://github.com/svddchallenge/ctrsvdd2024_baseline/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f1be015840088565","mcp_get_code":{"code_sha256":"f1be015840088565"}},{"arxiv_id":"2406.03111","paper":"/paper/singing-voice-graph-modeling-for-singfake","title":"Singing Voice Graph Modeling for SingFake Detection","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xjchengit/singgraph","path":"utils/eval_metrics.py","file_url":"https://github.com/xjchengit/singgraph/blob/HEAD/utils/eval_metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8042ce3c5ec2c9e5","mcp_get_code":{"code_sha256":"8042ce3c5ec2c9e5"}},{"arxiv_id":"2405.08363","paper":"/paper/unmarker-a-universal-attack-on-defensive","title":"UnMarker: A Universal Attack on Defensive Image Watermarking","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andrekassis/ai-watermark","path":"modules/metrics.py","file_url":"https://github.com/andrekassis/ai-watermark/blob/HEAD/modules/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7a62f8b12ad13309","mcp_get_code":{"code_sha256":"7a62f8b12ad13309"}},{"arxiv_id":"2309.07525","paper":"/paper/singfake-singing-voice-deepfake-detection","title":"SingFake: Singing Voice Deepfake Detection","date":"2023-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yongyizang/SingFake","path":"models/feat+resnet/eval_metrics.py","file_url":"https://github.com/yongyizang/SingFake/blob/HEAD/models/feat%2Bresnet/eval_metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"047cd2d292cdc66d","mcp_get_code":{"code_sha256":"047cd2d292cdc66d"}},{"arxiv_id":"2305.14035","paper":"/paper/can-self-supervised-neural-networks-pre","title":"Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idiap/ssl-caller-detection","path":"src/classifier_caller_groups.py","file_url":"https://github.com/idiap/ssl-caller-detection/blob/HEAD/src/classifier_caller_groups.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"f88dfdeca68a625f","mcp_get_code":{"code_sha256":"f88dfdeca68a625f"}},{"arxiv_id":"2210.02437","paper":"/paper/asvspoof-2021-towards-spoofed-and-deepfake","title":"ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild","date":null,"month_inferred_from_arxiv_id":"2022-10","title_source":"archive","repo":"asvspoof-challenge/2021","path":"eval-package/eval_metrics.py","file_url":"https://github.com/asvspoof-challenge/2021/blob/HEAD/eval-package/eval_metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"047cd2d292cdc66d","mcp_get_code":{"code_sha256":"047cd2d292cdc66d"}},{"arxiv_id":"2202.03218","paper":"/paper/efficient-adapter-transfer-of-self-supervised","title":"Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition","date":"2022-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sinhat98/adapter-wavlm","path":"ASV/utils.py","file_url":"https://github.com/sinhat98/adapter-wavlm/blob/HEAD/ASV/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d9e8cae2fb74061","mcp_get_code":{"code_sha256":"7d9e8cae2fb74061"}},{"arxiv_id":"2104.01320","paper":"/paper/an-empirical-study-on-channel-effects-for","title":"An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems","date":"2021-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yzyouzhang/asvspoof2021_air","path":"eval_metrics.py","file_url":"https://github.com/yzyouzhang/asvspoof2021_air/blob/HEAD/eval_metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"047cd2d292cdc66d","mcp_get_code":{"code_sha256":"047cd2d292cdc66d"}},{"arxiv_id":"2011.01108","paper":"/paper/end-to-end-anti-spoofing-with-rawnet2","title":"End-to-end anti-spoofing with RawNet2","date":"2020-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eurecom-asp/rawnet2-antispoofing","path":"tDCF_python/eval_metrics.py","file_url":"https://github.com/eurecom-asp/rawnet2-antispoofing/blob/HEAD/tDCF_python/eval_metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"047cd2d292cdc66d","mcp_get_code":{"code_sha256":"047cd2d292cdc66d"}},{"arxiv_id":"1904.05441","paper":"/paper/asvspoof-2019-future-horizons-in-spoofed-and","title":"ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection","date":"2019-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmirmohammadRostami/ASV-anti-spoofing-with-EABN","path":"eval_metrics.py","file_url":"https://github.com/AmirmohammadRostami/ASV-anti-spoofing-with-EABN/blob/HEAD/eval_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5b603b2f9b523155","mcp_get_code":{"code_sha256":"5b603b2f9b523155"}},{"arxiv_id":"1810.13048","paper":"/paper/attentive-filtering-networks-for-audio-replay","title":"Attentive Filtering Networks for Audio Replay Attack Detection","date":"2018-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jefflai108/Attentive-Filtering-Network","path":"src/v1_metrics.py","file_url":"https://github.com/jefflai108/Attentive-Filtering-Network/blob/HEAD/src/v1_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a44e9b62ee06507","mcp_get_code":{"code_sha256":"9a44e9b62ee06507"}}]}