{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:G42MDCVZXCVHDKUQ6JEWPWPK2V","short_pith_number":"pith:G42MDCVZ","schema_version":"1.0","canonical_sha256":"3734c18ab9b8aa71aa90f24967d9ead573baa95c2f139ee025a2b9a06212b8e1","source":{"kind":"arxiv","id":"1606.04189","version":2},"attestation_state":"computed","paper":{"title":"Inverting face embeddings with convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Andrey Zhmoginov, Mark Sandler","submitted_at":"2016-06-14T01:35:12Z","abstract_excerpt":"Deep neural networks have dramatically advanced the state of the art for many areas of machine learning. Recently they have been shown to have a remarkable ability to generate highly complex visual artifacts such as images and text rather than simply recognize them.\n  In this work we use neural networks to effectively invert low-dimensional face embeddings while producing realistically looking consistent images. Our contribution is twofold, first we show that a gradient ascent style approaches can be used to reproduce consistent images, with a help of a guiding image. Second, we demonstrate th"},"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":"1606.04189","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-14T01:35:12Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"87737b825635af92a3cf0977ed659e8b4853cf78dd25e92a2d84d2f02b01f65d","abstract_canon_sha256":"c39f2d6a5f487cac9088e2448b40f04e439c327c28ed95845b87b19ad527ad55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:22.380453Z","signature_b64":"HzL+0cdixLm3UKLIkeCq65YkDeAy5wHDF3TXj4X66BRoucbipx4rAzMfNwssxPLgyM9R4/2XrkUjracGWs4iCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3734c18ab9b8aa71aa90f24967d9ead573baa95c2f139ee025a2b9a06212b8e1","last_reissued_at":"2026-05-18T01:11:22.379488Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:22.379488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inverting face embeddings with convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Andrey Zhmoginov, Mark Sandler","submitted_at":"2016-06-14T01:35:12Z","abstract_excerpt":"Deep neural networks have dramatically advanced the state of the art for many areas of machine learning. Recently they have been shown to have a remarkable ability to generate highly complex visual artifacts such as images and text rather than simply recognize them.\n  In this work we use neural networks to effectively invert low-dimensional face embeddings while producing realistically looking consistent images. Our contribution is twofold, first we show that a gradient ascent style approaches can be used to reproduce consistent images, with a help of a guiding image. Second, we demonstrate th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.04189","kind":"arxiv","version":2},"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":"1606.04189","created_at":"2026-05-18T01:11:22.379640+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.04189v2","created_at":"2026-05-18T01:11:22.379640+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.04189","created_at":"2026-05-18T01:11:22.379640+00:00"},{"alias_kind":"pith_short_12","alias_value":"G42MDCVZXCVH","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"G42MDCVZXCVHDKUQ","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"G42MDCVZ","created_at":"2026-05-18T12:30:15.759754+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2402.02540","citing_title":"Embedding Non-Distortive Cancelable Face Template Generation","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03857","citing_title":"A Deeper Dive into the Irreversibility of PolyProtect: Making Protected Face Templates Harder to Invert","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V","json":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V.json","graph_json":"https://pith.science/api/pith-number/G42MDCVZXCVHDKUQ6JEWPWPK2V/graph.json","events_json":"https://pith.science/api/pith-number/G42MDCVZXCVHDKUQ6JEWPWPK2V/events.json","paper":"https://pith.science/paper/G42MDCVZ"},"agent_actions":{"view_html":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V","download_json":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V.json","view_paper":"https://pith.science/paper/G42MDCVZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.04189&json=true","fetch_graph":"https://pith.science/api/pith-number/G42MDCVZXCVHDKUQ6JEWPWPK2V/graph.json","fetch_events":"https://pith.science/api/pith-number/G42MDCVZXCVHDKUQ6JEWPWPK2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V/action/storage_attestation","attest_author":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V/action/author_attestation","sign_citation":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V/action/citation_signature","submit_replication":"https://pith.science/pith/G42MDCVZXCVHDKUQ6JEWPWPK2V/action/replication_record"}},"created_at":"2026-05-18T01:11:22.379640+00:00","updated_at":"2026-05-18T01:11:22.379640+00:00"}