{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6SOR6NMBMXI27KCHEN72QP73LM","short_pith_number":"pith:6SOR6NMB","schema_version":"1.0","canonical_sha256":"f49d1f358165d1afa847237fa83ffb5b350c53f735e95f8232b27ec935631f5c","source":{"kind":"arxiv","id":"1806.00580","version":1},"attestation_state":"computed","paper":{"title":"Detecting Adversarial Examples via Key-based Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jun Wang, Ou Wu, Pinlong Zhao, Qinghua Hu, Zhouyu Fu","submitted_at":"2018-06-02T04:13:02Z","abstract_excerpt":"Though deep neural networks have achieved state-of-the-art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful deep neural networks. Various defense methods have been proposed to address this issue. However, they either require knowledge on the process of generating adversarial examples, or are not robust against new attacks specifically designed to penetrate the existing defense. In this work, we introduce key-"},"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":"1806.00580","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-02T04:13:02Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"50fa65e785c71faaf95dc662f68c156e24b76ae8c0de302cdd25798c01b5f9e4","abstract_canon_sha256":"fa5ad1aed91c68dcb8d9372e4a037a24f5852c4e4822b6f77b1dc2731d4fe3c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:19.014630Z","signature_b64":"YCZKPidu17POqN8BWSUgrluUPRGlVaSX2ffueoscxM9OUHURWUuN5HwC18nIzgyOnuwjFct7dZ6tsDadi9iLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f49d1f358165d1afa847237fa83ffb5b350c53f735e95f8232b27ec935631f5c","last_reissued_at":"2026-05-18T00:14:19.014073Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:19.014073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Adversarial Examples via Key-based Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jun Wang, Ou Wu, Pinlong Zhao, Qinghua Hu, Zhouyu Fu","submitted_at":"2018-06-02T04:13:02Z","abstract_excerpt":"Though deep neural networks have achieved state-of-the-art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful deep neural networks. Various defense methods have been proposed to address this issue. However, they either require knowledge on the process of generating adversarial examples, or are not robust against new attacks specifically designed to penetrate the existing defense. In this work, we introduce key-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00580","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":""},"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":"1806.00580","created_at":"2026-05-18T00:14:19.014149+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.00580v1","created_at":"2026-05-18T00:14:19.014149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00580","created_at":"2026-05-18T00:14:19.014149+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SOR6NMBMXI2","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SOR6NMBMXI27KCH","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SOR6NMB","created_at":"2026-05-18T12:32:11.075285+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/6SOR6NMBMXI27KCHEN72QP73LM","json":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM.json","graph_json":"https://pith.science/api/pith-number/6SOR6NMBMXI27KCHEN72QP73LM/graph.json","events_json":"https://pith.science/api/pith-number/6SOR6NMBMXI27KCHEN72QP73LM/events.json","paper":"https://pith.science/paper/6SOR6NMB"},"agent_actions":{"view_html":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM","download_json":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM.json","view_paper":"https://pith.science/paper/6SOR6NMB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.00580&json=true","fetch_graph":"https://pith.science/api/pith-number/6SOR6NMBMXI27KCHEN72QP73LM/graph.json","fetch_events":"https://pith.science/api/pith-number/6SOR6NMBMXI27KCHEN72QP73LM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM/action/storage_attestation","attest_author":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM/action/author_attestation","sign_citation":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM/action/citation_signature","submit_replication":"https://pith.science/pith/6SOR6NMBMXI27KCHEN72QP73LM/action/replication_record"}},"created_at":"2026-05-18T00:14:19.014149+00:00","updated_at":"2026-05-18T00:14:19.014149+00:00"}