{"url":"/sota/face-anti-spoofing-on-celeba-spoof-enroll5","task":{"name":"Face Anti-Spoofing","url":"/task/face-anti-spoofing","note":null},"dataset":{"name":"CelebA-Spoof-Enroll5","url":"/dataset/celeba-spoof-enroll"},"category":"Computer Vision","categories":["Computer Vision","Miscellaneous","Music","Robots"],"category_note":null,"description":"Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Some examples of attacks:\r\n\r\n- **Print attack**: The attacker uses someone’s photo. The image is printed or displayed on a digital device.\r\n\r\n- **Replay/video attack**: A more sophisticated way to trick the system, which usually requires a looped video of a victim’s face. This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.\r\n\r\n- **3D mask attack**: During this type of attack, a mask is used as the tool of choice for spoofing. It’s an even more sophisticated attack than playing a face video. In addition to natural facial movements, it enables ways to deceive some extra layers of protection such as depth sensors.\r\n\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing](https://github.com/XgTu/GFA-CNN) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["AUC"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC":"higher"}},"counts":{"rows":5,"rows_with_code":5,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ResNet 18 Personalized","metrics":{"AUC":"99.2"},"uses_additional_data":false,"paper_date":"2022-01-05","paper":"/paper/a-personalized-benchmark-for-face-anti","paper_url":"https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html","paper_title":"A personalized benchmark for face anti-spoofing","code":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"VGG16 Personalized","metrics":{"AUC":"98.6"},"uses_additional_data":false,"paper_date":"2022-01-05","paper":"/paper/a-personalized-benchmark-for-face-anti","paper_url":"https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html","paper_title":"A personalized benchmark for face anti-spoofing","code":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"VGG16","metrics":{"AUC":"98.0"},"uses_additional_data":false,"paper_date":"2014-09-04","paper":"/paper/very-deep-convolutional-networks-for-large","paper_url":"http://arxiv.org/abs/1409.1556v6","paper_title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","code":"https://github.com/tensorflow/models/tree/master/research/slim","n_code_links":305,"syntology":{"n_ran":14,"n_unverified":108,"n_samples":122,"n_pointer_only_licence":4}},{"rank_in_archive_order":4,"model":"FeatherNet Personalized","metrics":{"AUC":"97.8"},"uses_additional_data":false,"paper_date":"2022-01-05","paper":"/paper/a-personalized-benchmark-for-face-anti","paper_url":"https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html","paper_title":"A personalized benchmark for face anti-spoofing","code":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"FeatherNet","metrics":{"AUC":"97.1"},"uses_additional_data":false,"paper_date":"2019-04-22","paper":"/paper/190409290","paper_url":"http://arxiv.org/abs/1904.09290v1","paper_title":"FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing","code":"https://github.com/SoftwareGift/FeatheNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019","n_code_links":3,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,885 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6885,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":14,"n_unverified":108,"n_samples":122,"n_pointer_only_licence":4,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":14,"n_unverified":108,"n_samples":122,"n_pointer_only_licence":4,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}