{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:36ATJ4S6K4PNQFRWYJTKLPUYEB","short_pith_number":"pith:36ATJ4S6","schema_version":"1.0","canonical_sha256":"df8134f25e571ed81636c266a5be982056d5699df2ea61ada007039c9e8066a4","source":{"kind":"arxiv","id":"1906.03749","version":1},"attestation_state":"computed","paper":{"title":"Improved Adversarial Robustness via Logit Regularization Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cecilia Summers, Michael J. Dinneen","submitted_at":"2019-06-10T00:51:44Z","abstract_excerpt":"While great progress has been made at making neural networks effective across a wide range of visual tasks, most models are surprisingly vulnerable. This frailness takes the form of small, carefully chosen perturbations of their input, known as adversarial examples, which represent a security threat for learned vision models in the wild -- a threat which should be responsibly defended against in safety-critical applications of computer vision. In this paper, we advocate for and experimentally investigate the use of a family of logit regularization techniques as an adversarial defense, which ca"},"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":"1906.03749","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-10T00:51:44Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"76f3b69963b73f814c1db93a4c9917aca5323266744d5f540d1ed8cbba897598","abstract_canon_sha256":"f81d28dda5740ddbcb6aabc13a64657b87822c6746284e11dcaf7de8fabb7696"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:45.078618Z","signature_b64":"Tr6tJNpYyTbbYzUfocY+xZbEKCuLzlQcrazpV+jVKsYHDfhualSSRM3OHPSqlF/tUDV3cA0nMNZudZXdSBBNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df8134f25e571ed81636c266a5be982056d5699df2ea61ada007039c9e8066a4","last_reissued_at":"2026-05-17T23:43:45.078050Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:45.078050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Adversarial Robustness via Logit Regularization Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cecilia Summers, Michael J. Dinneen","submitted_at":"2019-06-10T00:51:44Z","abstract_excerpt":"While great progress has been made at making neural networks effective across a wide range of visual tasks, most models are surprisingly vulnerable. This frailness takes the form of small, carefully chosen perturbations of their input, known as adversarial examples, which represent a security threat for learned vision models in the wild -- a threat which should be responsibly defended against in safety-critical applications of computer vision. In this paper, we advocate for and experimentally investigate the use of a family of logit regularization techniques as an adversarial defense, which ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03749","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":"1906.03749","created_at":"2026-05-17T23:43:45.078135+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.03749v1","created_at":"2026-05-17T23:43:45.078135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03749","created_at":"2026-05-17T23:43:45.078135+00:00"},{"alias_kind":"pith_short_12","alias_value":"36ATJ4S6K4PN","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"36ATJ4S6K4PNQFRW","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"36ATJ4S6","created_at":"2026-05-18T12:33:07.085635+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/36ATJ4S6K4PNQFRWYJTKLPUYEB","json":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB.json","graph_json":"https://pith.science/api/pith-number/36ATJ4S6K4PNQFRWYJTKLPUYEB/graph.json","events_json":"https://pith.science/api/pith-number/36ATJ4S6K4PNQFRWYJTKLPUYEB/events.json","paper":"https://pith.science/paper/36ATJ4S6"},"agent_actions":{"view_html":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB","download_json":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB.json","view_paper":"https://pith.science/paper/36ATJ4S6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.03749&json=true","fetch_graph":"https://pith.science/api/pith-number/36ATJ4S6K4PNQFRWYJTKLPUYEB/graph.json","fetch_events":"https://pith.science/api/pith-number/36ATJ4S6K4PNQFRWYJTKLPUYEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB/action/storage_attestation","attest_author":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB/action/author_attestation","sign_citation":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB/action/citation_signature","submit_replication":"https://pith.science/pith/36ATJ4S6K4PNQFRWYJTKLPUYEB/action/replication_record"}},"created_at":"2026-05-17T23:43:45.078135+00:00","updated_at":"2026-05-17T23:43:45.078135+00:00"}