{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JYRF56L45TEDV7KT4DY3HH5OC2","short_pith_number":"pith:JYRF56L4","schema_version":"1.0","canonical_sha256":"4e225ef97cecc83afd53e0f1b39fae169d725a8f81c0e901db2b73f8cf81f2d8","source":{"kind":"arxiv","id":"2302.01843","version":2},"attestation_state":"computed","paper":{"title":"MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fadi Boutros, Jan Niklas Kolf, Marco Huber, Meiling Fang, Naser Damer, Patrick Siebke","submitted_at":"2023-02-03T16:37:38Z","abstract_excerpt":"Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder model"},"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":"2302.01843","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-03T16:37:38Z","cross_cats_sorted":[],"title_canon_sha256":"b675ae20582771579737a3351e7dc50f5b942a060dfb2417d3e8acff9763e65a","abstract_canon_sha256":"ecbc3a8a275e64660ff8f4e1db0cf0d2247648d4f2937a4dd5ac17f8f3840cf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:13.080576Z","signature_b64":"oOfhJYs3JE4pvXkEvM2jbzqGy9vjnCGdr6XeUxf/39qBAqiokQaWAnhnE0KN3GuxrOdxJdP0Kqwsj2G442YkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e225ef97cecc83afd53e0f1b39fae169d725a8f81c0e901db2b73f8cf81f2d8","last_reissued_at":"2026-07-05T05:50:13.080054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:13.080054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fadi Boutros, Jan Niklas Kolf, Marco Huber, Meiling Fang, Naser Damer, Patrick Siebke","submitted_at":"2023-02-03T16:37:38Z","abstract_excerpt":"Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.01843","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.01843/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":"2302.01843","created_at":"2026-07-05T05:50:13.080114+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.01843v2","created_at":"2026-07-05T05:50:13.080114+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.01843","created_at":"2026-07-05T05:50:13.080114+00:00"},{"alias_kind":"pith_short_12","alias_value":"JYRF56L45TED","created_at":"2026-07-05T05:50:13.080114+00:00"},{"alias_kind":"pith_short_16","alias_value":"JYRF56L45TEDV7KT","created_at":"2026-07-05T05:50:13.080114+00:00"},{"alias_kind":"pith_short_8","alias_value":"JYRF56L4","created_at":"2026-07-05T05:50:13.080114+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.20585","citing_title":"On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2","json":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2.json","graph_json":"https://pith.science/api/pith-number/JYRF56L45TEDV7KT4DY3HH5OC2/graph.json","events_json":"https://pith.science/api/pith-number/JYRF56L45TEDV7KT4DY3HH5OC2/events.json","paper":"https://pith.science/paper/JYRF56L4"},"agent_actions":{"view_html":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2","download_json":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2.json","view_paper":"https://pith.science/paper/JYRF56L4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.01843&json=true","fetch_graph":"https://pith.science/api/pith-number/JYRF56L45TEDV7KT4DY3HH5OC2/graph.json","fetch_events":"https://pith.science/api/pith-number/JYRF56L45TEDV7KT4DY3HH5OC2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2/action/storage_attestation","attest_author":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2/action/author_attestation","sign_citation":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2/action/citation_signature","submit_replication":"https://pith.science/pith/JYRF56L45TEDV7KT4DY3HH5OC2/action/replication_record"}},"created_at":"2026-07-05T05:50:13.080114+00:00","updated_at":"2026-07-05T05:50:13.080114+00:00"}