{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:35HBZS7HBQPVVUJDZOXNAQIQB4","short_pith_number":"pith:35HBZS7H","schema_version":"1.0","canonical_sha256":"df4e1ccbe70c1f5ad123cbaed041100f250f4d78eb05f608289a4da8d54a43f2","source":{"kind":"arxiv","id":"2509.04214","version":1},"attestation_state":"computed","paper":{"title":"An Automated, Scalable Machine Learning Model Inversion Assessment Pipeline","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"David Saranchak, Jessica Carpenter, Nathaniel D. Bastian, Tyler Shumaker","submitted_at":"2025-09-04T13:39:37Z","abstract_excerpt":"Machine learning (ML) models have the potential to transform military battlefields, presenting a large external pressure to rapidly incorporate them into operational settings. However, it is well-established that these ML models are vulnerable to a number of adversarial attacks throughout the model deployment pipeline that threaten to negate battlefield advantage. One broad category is privacy attacks (such as model inversion) where an adversary can reverse engineer information from the model, such as the sensitive data used in its training. The ability to quantify the risk of model inversion "},"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":"2509.04214","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-09-04T13:39:37Z","cross_cats_sorted":[],"title_canon_sha256":"9292c75d4f1cfa79bebe277f8baecd39c665d464f8c71f7079fdd67461722ab1","abstract_canon_sha256":"53940d712789ad410020a8950344ce35e8c09e7e016a954351340b36f846c15b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:55.615991Z","signature_b64":"KM6WA9GEwVdF9TR+o1c4J5zGOUF/mlXf+nAZMSafE34NqtqUCzBlBVlOvpUeehaLhFIzIwZPEZj14fKa0icEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df4e1ccbe70c1f5ad123cbaed041100f250f4d78eb05f608289a4da8d54a43f2","last_reissued_at":"2026-07-05T12:04:55.615399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:55.615399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Automated, Scalable Machine Learning Model Inversion Assessment Pipeline","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"David Saranchak, Jessica Carpenter, Nathaniel D. Bastian, Tyler Shumaker","submitted_at":"2025-09-04T13:39:37Z","abstract_excerpt":"Machine learning (ML) models have the potential to transform military battlefields, presenting a large external pressure to rapidly incorporate them into operational settings. However, it is well-established that these ML models are vulnerable to a number of adversarial attacks throughout the model deployment pipeline that threaten to negate battlefield advantage. One broad category is privacy attacks (such as model inversion) where an adversary can reverse engineer information from the model, such as the sensitive data used in its training. The ability to quantify the risk of model inversion "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04214","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.04214/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":"2509.04214","created_at":"2026-07-05T12:04:55.615475+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.04214v1","created_at":"2026-07-05T12:04:55.615475+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04214","created_at":"2026-07-05T12:04:55.615475+00:00"},{"alias_kind":"pith_short_12","alias_value":"35HBZS7HBQPV","created_at":"2026-07-05T12:04:55.615475+00:00"},{"alias_kind":"pith_short_16","alias_value":"35HBZS7HBQPVVUJD","created_at":"2026-07-05T12:04:55.615475+00:00"},{"alias_kind":"pith_short_8","alias_value":"35HBZS7H","created_at":"2026-07-05T12:04:55.615475+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/35HBZS7HBQPVVUJDZOXNAQIQB4","json":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4.json","graph_json":"https://pith.science/api/pith-number/35HBZS7HBQPVVUJDZOXNAQIQB4/graph.json","events_json":"https://pith.science/api/pith-number/35HBZS7HBQPVVUJDZOXNAQIQB4/events.json","paper":"https://pith.science/paper/35HBZS7H"},"agent_actions":{"view_html":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4","download_json":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4.json","view_paper":"https://pith.science/paper/35HBZS7H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.04214&json=true","fetch_graph":"https://pith.science/api/pith-number/35HBZS7HBQPVVUJDZOXNAQIQB4/graph.json","fetch_events":"https://pith.science/api/pith-number/35HBZS7HBQPVVUJDZOXNAQIQB4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4/action/storage_attestation","attest_author":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4/action/author_attestation","sign_citation":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4/action/citation_signature","submit_replication":"https://pith.science/pith/35HBZS7HBQPVVUJDZOXNAQIQB4/action/replication_record"}},"created_at":"2026-07-05T12:04:55.615475+00:00","updated_at":"2026-07-05T12:04:55.615475+00:00"}