{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ITTNNZREJL7UBKGBE5ITSNEGVV","short_pith_number":"pith:ITTNNZRE","schema_version":"1.0","canonical_sha256":"44e6d6e6244aff40a8c12751393486ad6a59d57d18fee86759433092e38699bd","source":{"kind":"arxiv","id":"1908.09124","version":3},"attestation_state":"computed","paper":{"title":"SeesawFaceNets: sparse and robust face verification model for mobile platform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jintao Zhang","submitted_at":"2019-08-24T11:21:38Z","abstract_excerpt":"Deep Convolutional Neural Network (DCNNs) come to be the most widely used solution for most computer vision related tasks, and one of the most important application scenes is face verification. Due to its high-accuracy performance, deep face verification models of which the inference stage occurs on cloud platform through internet plays the key role on most prectical scenes. However, two critical issues exist: First, individual privacy may not be well protected since they have to upload their personal photo and other private information to the online cloud backend. Secondly, either training or"},"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":"1908.09124","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-24T11:21:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29bb6b49c27344c67bf90e56fff402791b6ee16100090e8ebe7e9a29b30a5500","abstract_canon_sha256":"f7e2073f49156160d382a3e75bd2d301367bf27c174a815f57b5c8a53efc8450"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:23:06.966160Z","signature_b64":"cQen1INXt06mb4SbfQ9dEFFlfwciYvy0kZHbIZwEjaC72w6XDLzK35SNX+xE4iv5JPek1XH1AcilY+wOiF5gBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44e6d6e6244aff40a8c12751393486ad6a59d57d18fee86759433092e38699bd","last_reissued_at":"2026-07-05T00:23:06.965735Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:23:06.965735Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SeesawFaceNets: sparse and robust face verification model for mobile platform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jintao Zhang","submitted_at":"2019-08-24T11:21:38Z","abstract_excerpt":"Deep Convolutional Neural Network (DCNNs) come to be the most widely used solution for most computer vision related tasks, and one of the most important application scenes is face verification. Due to its high-accuracy performance, deep face verification models of which the inference stage occurs on cloud platform through internet plays the key role on most prectical scenes. However, two critical issues exist: First, individual privacy may not be well protected since they have to upload their personal photo and other private information to the online cloud backend. Secondly, either training or"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09124","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.09124/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1908.09124","created_at":"2026-07-05T00:23:06.965792+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.09124v3","created_at":"2026-07-05T00:23:06.965792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09124","created_at":"2026-07-05T00:23:06.965792+00:00"},{"alias_kind":"pith_short_12","alias_value":"ITTNNZREJL7U","created_at":"2026-07-05T00:23:06.965792+00:00"},{"alias_kind":"pith_short_16","alias_value":"ITTNNZREJL7UBKGB","created_at":"2026-07-05T00:23:06.965792+00:00"},{"alias_kind":"pith_short_8","alias_value":"ITTNNZRE","created_at":"2026-07-05T00:23:06.965792+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/ITTNNZREJL7UBKGBE5ITSNEGVV","json":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV.json","graph_json":"https://pith.science/api/pith-number/ITTNNZREJL7UBKGBE5ITSNEGVV/graph.json","events_json":"https://pith.science/api/pith-number/ITTNNZREJL7UBKGBE5ITSNEGVV/events.json","paper":"https://pith.science/paper/ITTNNZRE"},"agent_actions":{"view_html":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV","download_json":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV.json","view_paper":"https://pith.science/paper/ITTNNZRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.09124&json=true","fetch_graph":"https://pith.science/api/pith-number/ITTNNZREJL7UBKGBE5ITSNEGVV/graph.json","fetch_events":"https://pith.science/api/pith-number/ITTNNZREJL7UBKGBE5ITSNEGVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV/action/storage_attestation","attest_author":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV/action/author_attestation","sign_citation":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV/action/citation_signature","submit_replication":"https://pith.science/pith/ITTNNZREJL7UBKGBE5ITSNEGVV/action/replication_record"}},"created_at":"2026-07-05T00:23:06.965792+00:00","updated_at":"2026-07-05T00:23:06.965792+00:00"}