{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YZWASEBMVTEM7XBQK3VC5I5XEM","short_pith_number":"pith:YZWASEBM","schema_version":"1.0","canonical_sha256":"c66c09102cacc8cfdc3056ea2ea3b7232d04eaf25b3ed450b19866212e4d6f9a","source":{"kind":"arxiv","id":"1901.03398","version":1},"attestation_state":"computed","paper":{"title":"Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin","submitted_at":"2019-01-10T21:14:11Z","abstract_excerpt":"The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning systems. Adversarial examples are similar (from a perceptual notion of similarity) to samples from the data distribution, that \"fool\" a machine learning classifier. For computer vision applications, these are images with carefully crafted but almost imperceptible changes, that are misclassified. In this work, we characterize this phenomenon under an existing taxonomy of threats to biometric systems, in particular identif"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.03398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-10T21:14:11Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"3d96688d7d1bc8442f8c2b4617edd277a17bc7d2769e6873ac268043ec6f1dbd","abstract_canon_sha256":"5752ede7ae30780c9749e086981c9595d13d19f123996398d9202e1c5c8bb294"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:38.124692Z","signature_b64":"oOmZtQ6dOz3gYxQpPW/BlNrtUezUYxy0MqkIYLjjU1aAZK79LQOW9SEsaWeWw4VyBNPAttOzLflAddpkkhMLDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c66c09102cacc8cfdc3056ea2ea3b7232d04eaf25b3ed450b19866212e4d6f9a","last_reissued_at":"2026-05-17T23:55:38.124283Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:38.124283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin","submitted_at":"2019-01-10T21:14:11Z","abstract_excerpt":"The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning systems. Adversarial examples are similar (from a perceptual notion of similarity) to samples from the data distribution, that \"fool\" a machine learning classifier. For computer vision applications, these are images with carefully crafted but almost imperceptible changes, that are misclassified. In this work, we characterize this phenomenon under an existing taxonomy of threats to biometric systems, in particular identif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03398","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.03398","created_at":"2026-05-17T23:55:38.124349+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03398v1","created_at":"2026-05-17T23:55:38.124349+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03398","created_at":"2026-05-17T23:55:38.124349+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZWASEBMVTEM","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZWASEBMVTEM7XBQ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZWASEBM","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM","json":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM.json","graph_json":"https://pith.science/api/pith-number/YZWASEBMVTEM7XBQK3VC5I5XEM/graph.json","events_json":"https://pith.science/api/pith-number/YZWASEBMVTEM7XBQK3VC5I5XEM/events.json","paper":"https://pith.science/paper/YZWASEBM"},"agent_actions":{"view_html":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM","download_json":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM.json","view_paper":"https://pith.science/paper/YZWASEBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03398&json=true","fetch_graph":"https://pith.science/api/pith-number/YZWASEBMVTEM7XBQK3VC5I5XEM/graph.json","fetch_events":"https://pith.science/api/pith-number/YZWASEBMVTEM7XBQK3VC5I5XEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM/action/storage_attestation","attest_author":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM/action/author_attestation","sign_citation":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM/action/citation_signature","submit_replication":"https://pith.science/pith/YZWASEBMVTEM7XBQK3VC5I5XEM/action/replication_record"}},"created_at":"2026-05-17T23:55:38.124349+00:00","updated_at":"2026-05-17T23:55:38.124349+00:00"}