{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UA6LFLMYNFXGOP7S3SIW5624GJ","short_pith_number":"pith:UA6LFLMY","schema_version":"1.0","canonical_sha256":"a03cb2ad98696e673ff2dc916efb5c326a3c4242f7d1e16ebdb7b9df697f82ec","source":{"kind":"arxiv","id":"1812.06815","version":1},"attestation_state":"computed","paper":{"title":"Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Fran\\c{c}ois Menet, Jos\\'e M. Fernandez, Michel Gagnon, Paul Berthier","submitted_at":"2018-12-17T14:55:41Z","abstract_excerpt":"Deep learning models are vulnerable to adversarial examples which are input samples modified in order to maximize the error on the system. We introduce Spartan Networks, resistant deep neural networks that do not require input preprocessing nor adversarial training. These networks have an adversarial layer designed to discard some information of the network, thus forcing the system to focus on relevant input. This is done using a new activation function to discard data. The added layer trains the neural network to filter-out usually-irrelevant parts of its input. Our performance evaluation sho"},"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":"1812.06815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-17T14:55:41Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"82703cc6d49059fbba5e7e474ee879381b944b081bf4d32171ebf1e69821b0c3","abstract_canon_sha256":"5cc11d8664f2ab49690cd34c43f3e3843b92d2b5c43cea386b0fb325fad4157b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:09.304563Z","signature_b64":"EnR77fzaAou38Xv69vhfxkMjG+rO4oY3aH81QNKmpr2LBLE4e10F3ze/V0mDrev8qC7nmJXMIg4Le3xoLi7ABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a03cb2ad98696e673ff2dc916efb5c326a3c4242f7d1e16ebdb7b9df697f82ec","last_reissued_at":"2026-05-17T23:58:09.303956Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:09.303956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Fran\\c{c}ois Menet, Jos\\'e M. Fernandez, Michel Gagnon, Paul Berthier","submitted_at":"2018-12-17T14:55:41Z","abstract_excerpt":"Deep learning models are vulnerable to adversarial examples which are input samples modified in order to maximize the error on the system. We introduce Spartan Networks, resistant deep neural networks that do not require input preprocessing nor adversarial training. These networks have an adversarial layer designed to discard some information of the network, thus forcing the system to focus on relevant input. This is done using a new activation function to discard data. The added layer trains the neural network to filter-out usually-irrelevant parts of its input. Our performance evaluation sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06815","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":"1812.06815","created_at":"2026-05-17T23:58:09.304042+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.06815v1","created_at":"2026-05-17T23:58:09.304042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06815","created_at":"2026-05-17T23:58:09.304042+00:00"},{"alias_kind":"pith_short_12","alias_value":"UA6LFLMYNFXG","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UA6LFLMYNFXGOP7S","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UA6LFLMY","created_at":"2026-05-18T12:32:56.356000+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/UA6LFLMYNFXGOP7S3SIW5624GJ","json":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ.json","graph_json":"https://pith.science/api/pith-number/UA6LFLMYNFXGOP7S3SIW5624GJ/graph.json","events_json":"https://pith.science/api/pith-number/UA6LFLMYNFXGOP7S3SIW5624GJ/events.json","paper":"https://pith.science/paper/UA6LFLMY"},"agent_actions":{"view_html":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ","download_json":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ.json","view_paper":"https://pith.science/paper/UA6LFLMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.06815&json=true","fetch_graph":"https://pith.science/api/pith-number/UA6LFLMYNFXGOP7S3SIW5624GJ/graph.json","fetch_events":"https://pith.science/api/pith-number/UA6LFLMYNFXGOP7S3SIW5624GJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ/action/storage_attestation","attest_author":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ/action/author_attestation","sign_citation":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ/action/citation_signature","submit_replication":"https://pith.science/pith/UA6LFLMYNFXGOP7S3SIW5624GJ/action/replication_record"}},"created_at":"2026-05-17T23:58:09.304042+00:00","updated_at":"2026-05-17T23:58:09.304042+00:00"}