{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GUPMZTOC7XDSOBMVRLTOCTVNA5","short_pith_number":"pith:GUPMZTOC","schema_version":"1.0","canonical_sha256":"351ecccdc2fdc72705958ae6e14ead076a7434c87ae2c0005bd2fb15df8cc7a2","source":{"kind":"arxiv","id":"2409.01062","version":4},"attestation_state":"computed","paper":{"title":"Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Alex Kot, Hans Vandierendonck, Ira Assent, Ngai-Man Cheung, Ngoc-Bao Nguyen, Son T. Mai, Viet-Hung Tran","submitted_at":"2024-09-02T08:37:17Z","abstract_excerpt":"Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate on model-centric approaches, the impact of data on MI robustness remains largely unexplored. In this work, we explore Random Erasing (RE), a technique traditionally used for improving model generalization under occlusion, and uncover its surprising effectiveness as a defense against MI attacks. Specifically, our novel feature space analysis shows that models trained with RE-images introduce a significant discrepancy b"},"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":"2409.01062","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-02T08:37:17Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"0de379b93f9d2fa428586c70ed2ee5088cc33bb16ed043005bf2f75c87f6c7be","abstract_canon_sha256":"5ff8074c1e345151d25ce433ef3175e3a2bf64fe64e6738c38500f9de8129c4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:04:31.145625Z","signature_b64":"p+zc8yOZyilv7C9VgWwuFf4sWGynwkJwl9dRn4EZA6H0MiJKh/TrMH7Eu5M5h1fnlW0FOm+4w2GSktR4DU0VBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"351ecccdc2fdc72705958ae6e14ead076a7434c87ae2c0005bd2fb15df8cc7a2","last_reissued_at":"2026-06-02T03:04:31.145133Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:04:31.145133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Alex Kot, Hans Vandierendonck, Ira Assent, Ngai-Man Cheung, Ngoc-Bao Nguyen, Son T. Mai, Viet-Hung Tran","submitted_at":"2024-09-02T08:37:17Z","abstract_excerpt":"Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate on model-centric approaches, the impact of data on MI robustness remains largely unexplored. In this work, we explore Random Erasing (RE), a technique traditionally used for improving model generalization under occlusion, and uncover its surprising effectiveness as a defense against MI attacks. Specifically, our novel feature space analysis shows that models trained with RE-images introduce a significant discrepancy b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01062","kind":"arxiv","version":4},"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/2409.01062/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":"2409.01062","created_at":"2026-06-02T03:04:31.145193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.01062v4","created_at":"2026-06-02T03:04:31.145193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01062","created_at":"2026-06-02T03:04:31.145193+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUPMZTOC7XDS","created_at":"2026-06-02T03:04:31.145193+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUPMZTOC7XDSOBMV","created_at":"2026-06-02T03:04:31.145193+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUPMZTOC","created_at":"2026-06-02T03:04:31.145193+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/GUPMZTOC7XDSOBMVRLTOCTVNA5","json":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5.json","graph_json":"https://pith.science/api/pith-number/GUPMZTOC7XDSOBMVRLTOCTVNA5/graph.json","events_json":"https://pith.science/api/pith-number/GUPMZTOC7XDSOBMVRLTOCTVNA5/events.json","paper":"https://pith.science/paper/GUPMZTOC"},"agent_actions":{"view_html":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5","download_json":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5.json","view_paper":"https://pith.science/paper/GUPMZTOC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.01062&json=true","fetch_graph":"https://pith.science/api/pith-number/GUPMZTOC7XDSOBMVRLTOCTVNA5/graph.json","fetch_events":"https://pith.science/api/pith-number/GUPMZTOC7XDSOBMVRLTOCTVNA5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5/action/storage_attestation","attest_author":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5/action/author_attestation","sign_citation":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5/action/citation_signature","submit_replication":"https://pith.science/pith/GUPMZTOC7XDSOBMVRLTOCTVNA5/action/replication_record"}},"created_at":"2026-06-02T03:04:31.145193+00:00","updated_at":"2026-06-02T03:04:31.145193+00:00"}