{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:C5LHBECO6L2AAMRQWM47JLSM7M","short_pith_number":"pith:C5LHBECO","schema_version":"1.0","canonical_sha256":"175670904ef2f4003230b339f4ae4cfb2b041fdd0efac93cc1c72fa50bf05348","source":{"kind":"arxiv","id":"2103.04565","version":3},"attestation_state":"computed","paper":{"title":"Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Haimin Zhang, Min Xu","submitted_at":"2021-03-08T06:27:24Z","abstract_excerpt":"Deep neural networks have been successfully applied in various machine learning tasks. However, studies show that neural networks are susceptible to adversarial attacks. This exposes a potential threat to neural network-based intelligent systems. We observe that the probability of the correct result outputted by the neural network increases by applying small first-order perturbations generated for non-predicted class labels to adversarial examples. Based on this observation, we propose a method for counteracting adversarial perturbations to improve adversarial robustness. In the proposed metho"},"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":"2103.04565","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-08T06:27:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"33c19af6cfb347123ae52bc2d4f8dd0616913cdb8e551df3c081ac74fddcd00a","abstract_canon_sha256":"89f4401dc00d1b6e29aef5dde2a4b0fbf47d68824b98fc6d3b3d600ac41fe46f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:23.217565Z","signature_b64":"YwEwkj81X/4iuQkt2X3rLpb01Th0syMVpCWN47bkZOGaFWzN5U7IbxlSHt3uIzZOZ6l2D2YMnnWAOXpKLSaBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"175670904ef2f4003230b339f4ae4cfb2b041fdd0efac93cc1c72fa50bf05348","last_reissued_at":"2026-07-05T07:38:23.216992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:23.216992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Haimin Zhang, Min Xu","submitted_at":"2021-03-08T06:27:24Z","abstract_excerpt":"Deep neural networks have been successfully applied in various machine learning tasks. However, studies show that neural networks are susceptible to adversarial attacks. This exposes a potential threat to neural network-based intelligent systems. We observe that the probability of the correct result outputted by the neural network increases by applying small first-order perturbations generated for non-predicted class labels to adversarial examples. Based on this observation, we propose a method for counteracting adversarial perturbations to improve adversarial robustness. In the proposed metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.04565","kind":"arxiv","version":3},"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/2103.04565/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":"2103.04565","created_at":"2026-07-05T07:38:23.217075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.04565v3","created_at":"2026-07-05T07:38:23.217075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.04565","created_at":"2026-07-05T07:38:23.217075+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5LHBECO6L2A","created_at":"2026-07-05T07:38:23.217075+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5LHBECO6L2AAMRQ","created_at":"2026-07-05T07:38:23.217075+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5LHBECO","created_at":"2026-07-05T07:38:23.217075+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/C5LHBECO6L2AAMRQWM47JLSM7M","json":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M.json","graph_json":"https://pith.science/api/pith-number/C5LHBECO6L2AAMRQWM47JLSM7M/graph.json","events_json":"https://pith.science/api/pith-number/C5LHBECO6L2AAMRQWM47JLSM7M/events.json","paper":"https://pith.science/paper/C5LHBECO"},"agent_actions":{"view_html":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M","download_json":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M.json","view_paper":"https://pith.science/paper/C5LHBECO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.04565&json=true","fetch_graph":"https://pith.science/api/pith-number/C5LHBECO6L2AAMRQWM47JLSM7M/graph.json","fetch_events":"https://pith.science/api/pith-number/C5LHBECO6L2AAMRQWM47JLSM7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M/action/storage_attestation","attest_author":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M/action/author_attestation","sign_citation":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M/action/citation_signature","submit_replication":"https://pith.science/pith/C5LHBECO6L2AAMRQWM47JLSM7M/action/replication_record"}},"created_at":"2026-07-05T07:38:23.217075+00:00","updated_at":"2026-07-05T07:38:23.217075+00:00"}