{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SW6Q6II7ZCNMR5PNOLGUK4PVM6","short_pith_number":"pith:SW6Q6II7","canonical_record":{"source":{"id":"2403.09441","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-14T14:34:25Z","cross_cats_sorted":[],"title_canon_sha256":"15cdaeef102cb9b78a9f158894221d9cb76873583b5b298a28ebe53ff3b55505","abstract_canon_sha256":"d7c1336b7c8389758acbdbd1c5e692a72efbc1bc1bb0fd6bb7583ea929a024bc"},"schema_version":"1.0"},"canonical_sha256":"95bd0f211fc89ac8f5ed72cd4571f56787d1fec3fa8eee8ad561a753fb4e9b1f","source":{"kind":"arxiv","id":"2403.09441","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09441","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09441v2","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09441","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"SW6Q6II7ZCNM","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"SW6Q6II7ZCNMR5PN","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"SW6Q6II7","created_at":"2026-05-28T01:04:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SW6Q6II7ZCNMR5PNOLGUK4PVM6","target":"record","payload":{"canonical_record":{"source":{"id":"2403.09441","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-14T14:34:25Z","cross_cats_sorted":[],"title_canon_sha256":"15cdaeef102cb9b78a9f158894221d9cb76873583b5b298a28ebe53ff3b55505","abstract_canon_sha256":"d7c1336b7c8389758acbdbd1c5e692a72efbc1bc1bb0fd6bb7583ea929a024bc"},"schema_version":"1.0"},"canonical_sha256":"95bd0f211fc89ac8f5ed72cd4571f56787d1fec3fa8eee8ad561a753fb4e9b1f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:24.078965Z","signature_b64":"JhbAFXnW8zjIfJP+wW7J0Wwc/NIoW+8Sfuq3w6T20u87QZtl8Y3BFKhIM1APfJvykxCREBmwXVZKy6eGm4sJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95bd0f211fc89ac8f5ed72cd4571f56787d1fec3fa8eee8ad561a753fb4e9b1f","last_reissued_at":"2026-05-28T01:04:24.078442Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:24.078442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.09441","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-28T01:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q3Axc74xkC3BYU+pDfYesEx7DFTlxLs8dj3Fs8tEpLOC/PrlHbPBzQdhD92vnTYnb7Lx6ELpnlwI3m7EAXi9DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T21:14:35.702904Z"},"content_sha256":"e6e5903a9ff72a79341c7ce914c25581e47181a2614b34b7defc22ea6267c0d9","schema_version":"1.0","event_id":"sha256:e6e5903a9ff72a79341c7ce914c25581e47181a2614b34b7defc22ea6267c0d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SW6Q6II7ZCNMR5PNOLGUK4PVM6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel I R Cruz, Hallgrimur Thorsteinsson, Raghavendra Selvan, Tong Chen, Valdemar J Henriksen","submitted_at":"2024-03-14T14:34:25Z","abstract_excerpt":"As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy that can result in more robust models. This adversarial robustness comes with additional computational costs required to design adversarial attacks during training. The two objectives -- adversarial rob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09441","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/2403.09441/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":"2026-05-28T01:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5ncniU9hD1/ZLLeXh5MEmFr+kqGKqinmPDuX3zh9NaWo0tQK45Fg5FMo0dPlMSuiE1PSRv0+giEcm/ZhkjqYCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T21:14:35.703729Z"},"content_sha256":"f3dee5c1941191075966a28045d4060880ad3e7bae4961b1d6890f1c52bf8793","schema_version":"1.0","event_id":"sha256:f3dee5c1941191075966a28045d4060880ad3e7bae4961b1d6890f1c52bf8793"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/bundle.json","state_url":"https://pith.science/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/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":"2026-06-05T21:14:35Z","links":{"resolver":"https://pith.science/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6","bundle":"https://pith.science/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/bundle.json","state":"https://pith.science/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SW6Q6II7ZCNMR5PNOLGUK4PVM6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SW6Q6II7ZCNMR5PNOLGUK4PVM6","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":"d7c1336b7c8389758acbdbd1c5e692a72efbc1bc1bb0fd6bb7583ea929a024bc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-14T14:34:25Z","title_canon_sha256":"15cdaeef102cb9b78a9f158894221d9cb76873583b5b298a28ebe53ff3b55505"},"schema_version":"1.0","source":{"id":"2403.09441","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09441","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09441v2","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09441","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"SW6Q6II7ZCNM","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"SW6Q6II7ZCNMR5PN","created_at":"2026-05-28T01:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"SW6Q6II7","created_at":"2026-05-28T01:04:24Z"}],"graph_snapshots":[{"event_id":"sha256:f3dee5c1941191075966a28045d4060880ad3e7bae4961b1d6890f1c52bf8793","target":"graph","created_at":"2026-05-28T01:04:24Z","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/2403.09441/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy that can result in more robust models. This adversarial robustness comes with additional computational costs required to design adversarial attacks during training. The two objectives -- adversarial rob","authors_text":"Daniel I R Cruz, Hallgrimur Thorsteinsson, Raghavendra Selvan, Tong Chen, Valdemar J Henriksen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-14T14:34:25Z","title":"Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09441","kind":"arxiv","version":2},"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:e6e5903a9ff72a79341c7ce914c25581e47181a2614b34b7defc22ea6267c0d9","target":"record","created_at":"2026-05-28T01:04:24Z","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":"d7c1336b7c8389758acbdbd1c5e692a72efbc1bc1bb0fd6bb7583ea929a024bc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-14T14:34:25Z","title_canon_sha256":"15cdaeef102cb9b78a9f158894221d9cb76873583b5b298a28ebe53ff3b55505"},"schema_version":"1.0","source":{"id":"2403.09441","kind":"arxiv","version":2}},"canonical_sha256":"95bd0f211fc89ac8f5ed72cd4571f56787d1fec3fa8eee8ad561a753fb4e9b1f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"95bd0f211fc89ac8f5ed72cd4571f56787d1fec3fa8eee8ad561a753fb4e9b1f","first_computed_at":"2026-05-28T01:04:24.078442Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:24.078442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JhbAFXnW8zjIfJP+wW7J0Wwc/NIoW+8Sfuq3w6T20u87QZtl8Y3BFKhIM1APfJvykxCREBmwXVZKy6eGm4sJBA==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:24.078965Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.09441","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6e5903a9ff72a79341c7ce914c25581e47181a2614b34b7defc22ea6267c0d9","sha256:f3dee5c1941191075966a28045d4060880ad3e7bae4961b1d6890f1c52bf8793"],"state_sha256":"99db5296f509b4ceadb6a647276f710c56054e18e80298e92e18e53fbc7a12d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SxOXh1Nfq9bPoqDY4CZOemRST8fAIO+7TBkm78IsykqYpUiQjIKxNv05A90796jjOXM4/RXdG+NqlxQyqQgsBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T21:14:35.707749Z","bundle_sha256":"ac46201a6b06b2782bbe1e406e23e969f0f0f9c31b5097d3749a76f787ce59c6"}}