{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TO3WWBTCOLC23FKOK6SKP5DC3C","short_pith_number":"pith:TO3WWBTC","canonical_record":{"source":{"id":"2508.10065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-13T08:05:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1ff6d127f97a049c5f7fd9b2c886ffd4a1e7d9c0cde56add2b49d5f842158bd5","abstract_canon_sha256":"0bd24dfc93aea3183fb28e784692d3ba995c74fad91d3547f620afe10045c777"},"schema_version":"1.0"},"canonical_sha256":"9bb76b066272c5ad954e57a4a7f462d89296e8472718698b2d0b75e72d9df3ae","source":{"kind":"arxiv","id":"2508.10065","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.10065","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"2508.10065v1","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10065","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"TO3WWBTCOLC2","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_16","alias_value":"TO3WWBTCOLC23FKO","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_8","alias_value":"TO3WWBTC","created_at":"2026-07-05T11:53:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TO3WWBTCOLC23FKOK6SKP5DC3C","target":"record","payload":{"canonical_record":{"source":{"id":"2508.10065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-13T08:05:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1ff6d127f97a049c5f7fd9b2c886ffd4a1e7d9c0cde56add2b49d5f842158bd5","abstract_canon_sha256":"0bd24dfc93aea3183fb28e784692d3ba995c74fad91d3547f620afe10045c777"},"schema_version":"1.0"},"canonical_sha256":"9bb76b066272c5ad954e57a4a7f462d89296e8472718698b2d0b75e72d9df3ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:30.277415Z","signature_b64":"6XOWeSqJ+BSR2AVnZkE+z+utfK1BHjz/OSryVDeFTDLvleEsJSk9o8PxeVT+kWlEnrB7wksScrnxZiSBRIY2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bb76b066272c5ad954e57a4a7f462d89296e8472718698b2d0b75e72d9df3ae","last_reissued_at":"2026-07-05T11:53:30.276861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:30.276861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.10065","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Zu7p04vHXr9z57AEIBIDjMitFp8YSE3XTEceAFyl9w1j9MAz+4iH+Vdcgy/vB/oDWygirOBG9T0+zzmGvCZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:15:08.838574Z"},"content_sha256":"41fe28f2568838b6b4a65dc98d07ec8e8a56a4ec41e4edf0be74e3b08a185ab4","schema_version":"1.0","event_id":"sha256:41fe28f2568838b6b4a65dc98d07ec8e8a56a4ec41e4edf0be74e3b08a185ab4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TO3WWBTCOLC23FKOK6SKP5DC3C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CR","authors_text":"Gaowen Liu, Hongtao Xie, Sijia Liu, Yihua Zhang, Yuhao Sun","submitted_at":"2025-08-13T08:05:47Z","abstract_excerpt":"With the increasing demand for the right to be forgotten, machine unlearning (MU) has emerged as a vital tool for enhancing trust and regulatory compliance by enabling the removal of sensitive data influences from machine learning (ML) models. However, most MU algorithms primarily rely on in-training methods to adjust model weights, with limited exploration of the benefits that data-level adjustments could bring to the unlearning process. To address this gap, we propose a novel approach that leverages digital watermarking to facilitate MU by strategically modifying data content. By integrating"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10065","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/2508.10065/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xbD6bifwlKZcYTMuLLGuo9exBKhLciKsstLyWuz/qmBAyFQXRtwlZnxogAHPaep/rRyRWEpnbh6Y++g9xpIuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:15:08.839062Z"},"content_sha256":"821532307b984d11e4f269f82fc7f1c15a7d3c76388c51480451b4a6f2188d4c","schema_version":"1.0","event_id":"sha256:821532307b984d11e4f269f82fc7f1c15a7d3c76388c51480451b4a6f2188d4c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/bundle.json","state_url":"https://pith.science/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T06:15:08Z","links":{"resolver":"https://pith.science/pith/TO3WWBTCOLC23FKOK6SKP5DC3C","bundle":"https://pith.science/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/bundle.json","state":"https://pith.science/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TO3WWBTCOLC23FKOK6SKP5DC3C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TO3WWBTCOLC23FKOK6SKP5DC3C","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0bd24dfc93aea3183fb28e784692d3ba995c74fad91d3547f620afe10045c777","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-13T08:05:47Z","title_canon_sha256":"1ff6d127f97a049c5f7fd9b2c886ffd4a1e7d9c0cde56add2b49d5f842158bd5"},"schema_version":"1.0","source":{"id":"2508.10065","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.10065","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"2508.10065v1","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10065","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"TO3WWBTCOLC2","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_16","alias_value":"TO3WWBTCOLC23FKO","created_at":"2026-07-05T11:53:30Z"},{"alias_kind":"pith_short_8","alias_value":"TO3WWBTC","created_at":"2026-07-05T11:53:30Z"}],"graph_snapshots":[{"event_id":"sha256:821532307b984d11e4f269f82fc7f1c15a7d3c76388c51480451b4a6f2188d4c","target":"graph","created_at":"2026-07-05T11:53:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.10065/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the increasing demand for the right to be forgotten, machine unlearning (MU) has emerged as a vital tool for enhancing trust and regulatory compliance by enabling the removal of sensitive data influences from machine learning (ML) models. However, most MU algorithms primarily rely on in-training methods to adjust model weights, with limited exploration of the benefits that data-level adjustments could bring to the unlearning process. To address this gap, we propose a novel approach that leverages digital watermarking to facilitate MU by strategically modifying data content. By integrating","authors_text":"Gaowen Liu, Hongtao Xie, Sijia Liu, Yihua Zhang, Yuhao Sun","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-13T08:05:47Z","title":"Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10065","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:41fe28f2568838b6b4a65dc98d07ec8e8a56a4ec41e4edf0be74e3b08a185ab4","target":"record","created_at":"2026-07-05T11:53:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0bd24dfc93aea3183fb28e784692d3ba995c74fad91d3547f620afe10045c777","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-08-13T08:05:47Z","title_canon_sha256":"1ff6d127f97a049c5f7fd9b2c886ffd4a1e7d9c0cde56add2b49d5f842158bd5"},"schema_version":"1.0","source":{"id":"2508.10065","kind":"arxiv","version":1}},"canonical_sha256":"9bb76b066272c5ad954e57a4a7f462d89296e8472718698b2d0b75e72d9df3ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9bb76b066272c5ad954e57a4a7f462d89296e8472718698b2d0b75e72d9df3ae","first_computed_at":"2026-07-05T11:53:30.276861Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:30.276861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6XOWeSqJ+BSR2AVnZkE+z+utfK1BHjz/OSryVDeFTDLvleEsJSk9o8PxeVT+kWlEnrB7wksScrnxZiSBRIY2DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:30.277415Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.10065","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41fe28f2568838b6b4a65dc98d07ec8e8a56a4ec41e4edf0be74e3b08a185ab4","sha256:821532307b984d11e4f269f82fc7f1c15a7d3c76388c51480451b4a6f2188d4c"],"state_sha256":"1118a827e9e59ddbe31e384abbcc160889610a737cdf06fc6ed738a52b09e2f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZuX6Ag+ieH527oTUmvs74WfQ1V4S4ahm1OZufuYVSMiN/9ll3wmW2XpZ/8A40vAAcmq3JJkS4UGE2eMzK5ETCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:15:08.842440Z","bundle_sha256":"27308c7d3a4f13e747c0992fe96c4aa6d9f17aac443bd58cf27ebc1651495453"}}