{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:45SQDXGAZTJAO5A4CLCHSKMHKW","short_pith_number":"pith:45SQDXGA","schema_version":"1.0","canonical_sha256":"e76501dcc0ccd207741c12c4792987559f91c624302bbd470d386a251d363c2c","source":{"kind":"arxiv","id":"2206.08316","version":2},"attestation_state":"computed","paper":{"title":"Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Dingcheng Yang, Wenjian Yu, Zihao Xiao","submitted_at":"2022-06-16T17:22:40Z","abstract_excerpt":"Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to the transfer-based attack where the adversarial examples generated by a surrogate model are used to conduct black-box attacks. There are some work on generating the adversarial examples from a given surrogate model with better transferability. However, training a special surrogate model to generate adversarial examples with better transferability i"},"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":"2206.08316","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-16T17:22:40Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"70c951ff1bf8386bedf59de0c0a41996aaaac003cc4a090381c22d1444843413","abstract_canon_sha256":"3129047cc02caf42a6039bdb4c6043e192f2c9e436908d6d3c511ddba55d72d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:13.421356Z","signature_b64":"oLNhtYxS0DJKGNh5xZN5W3Ob3gC0cCvsFTcQyd4tNoRCndn5cWlkudDml3eskt4wEDmdgy022uY0y4WNBug3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e76501dcc0ccd207741c12c4792987559f91c624302bbd470d386a251d363c2c","last_reissued_at":"2026-07-05T06:47:13.420955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:13.420955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Dingcheng Yang, Wenjian Yu, Zihao Xiao","submitted_at":"2022-06-16T17:22:40Z","abstract_excerpt":"Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to the transfer-based attack where the adversarial examples generated by a surrogate model are used to conduct black-box attacks. There are some work on generating the adversarial examples from a given surrogate model with better transferability. However, training a special surrogate model to generate adversarial examples with better transferability i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08316","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/2206.08316/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":"2206.08316","created_at":"2026-07-05T06:47:13.421007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.08316v2","created_at":"2026-07-05T06:47:13.421007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08316","created_at":"2026-07-05T06:47:13.421007+00:00"},{"alias_kind":"pith_short_12","alias_value":"45SQDXGAZTJA","created_at":"2026-07-05T06:47:13.421007+00:00"},{"alias_kind":"pith_short_16","alias_value":"45SQDXGAZTJAO5A4","created_at":"2026-07-05T06:47:13.421007+00:00"},{"alias_kind":"pith_short_8","alias_value":"45SQDXGA","created_at":"2026-07-05T06:47:13.421007+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.22801","citing_title":"Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW","json":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW.json","graph_json":"https://pith.science/api/pith-number/45SQDXGAZTJAO5A4CLCHSKMHKW/graph.json","events_json":"https://pith.science/api/pith-number/45SQDXGAZTJAO5A4CLCHSKMHKW/events.json","paper":"https://pith.science/paper/45SQDXGA"},"agent_actions":{"view_html":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW","download_json":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW.json","view_paper":"https://pith.science/paper/45SQDXGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.08316&json=true","fetch_graph":"https://pith.science/api/pith-number/45SQDXGAZTJAO5A4CLCHSKMHKW/graph.json","fetch_events":"https://pith.science/api/pith-number/45SQDXGAZTJAO5A4CLCHSKMHKW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW/action/storage_attestation","attest_author":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW/action/author_attestation","sign_citation":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW/action/citation_signature","submit_replication":"https://pith.science/pith/45SQDXGAZTJAO5A4CLCHSKMHKW/action/replication_record"}},"created_at":"2026-07-05T06:47:13.421007+00:00","updated_at":"2026-07-05T06:47:13.421007+00:00"}