{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NTNGR354EERZO4CJBDE7CBJI3F","short_pith_number":"pith:NTNGR354","schema_version":"1.0","canonical_sha256":"6cda68efbc212397704908c9f10528d97354b9d66d6e8670221185d5b550b13f","source":{"kind":"arxiv","id":"1805.10204","version":1},"attestation_state":"computed","paper":{"title":"Adversarial examples from computational constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.LG"],"primary_cat":"stat.ML","authors_text":"Eric Price, Ilya Razenshteyn, S\\'ebastien Bubeck","submitted_at":"2018-05-25T15:39:06Z","abstract_excerpt":"Why are classifiers in high dimension vulnerable to \"adversarial\" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints.\n  First we prove that, for a broad set of classification tasks, the mere existence of a robust classifier implies that it can be found by a possibly exponential-time algorithm with relatively few training examples. Then we give a particular classification task where learning a robust classifier is computationally intractable. More precisely we construct a binary classification task in hi"},"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":"1805.10204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T15:39:06Z","cross_cats_sorted":["cs.CC","cs.LG"],"title_canon_sha256":"a8d9cd6a0cdf6dac2f14307b9b3c9cacd41f8c850871bdc8d8c264259c8bac77","abstract_canon_sha256":"19f9df56659c8a8ebba7f0e027409478b18bbbf0503b0dd76a0fba3434f43095"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:57.973982Z","signature_b64":"hmyDtQf2/JnM7RtoqrFUvte8ofxeBLq17PfoyH88GaQavXTgB7C+e/uZTgJ+4U0+hf7tm0oDIUfC6qnf2LkCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6cda68efbc212397704908c9f10528d97354b9d66d6e8670221185d5b550b13f","last_reissued_at":"2026-05-18T00:14:57.973495Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:57.973495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial examples from computational constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.LG"],"primary_cat":"stat.ML","authors_text":"Eric Price, Ilya Razenshteyn, S\\'ebastien Bubeck","submitted_at":"2018-05-25T15:39:06Z","abstract_excerpt":"Why are classifiers in high dimension vulnerable to \"adversarial\" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints.\n  First we prove that, for a broad set of classification tasks, the mere existence of a robust classifier implies that it can be found by a possibly exponential-time algorithm with relatively few training examples. Then we give a particular classification task where learning a robust classifier is computationally intractable. More precisely we construct a binary classification task in hi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10204","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":"1805.10204","created_at":"2026-05-18T00:14:57.973568+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.10204v1","created_at":"2026-05-18T00:14:57.973568+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10204","created_at":"2026-05-18T00:14:57.973568+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTNGR354EERZ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTNGR354EERZO4CJ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTNGR354","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.02658","citing_title":"Random Directional Attack for Fooling Deep Neural Networks","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F","json":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F.json","graph_json":"https://pith.science/api/pith-number/NTNGR354EERZO4CJBDE7CBJI3F/graph.json","events_json":"https://pith.science/api/pith-number/NTNGR354EERZO4CJBDE7CBJI3F/events.json","paper":"https://pith.science/paper/NTNGR354"},"agent_actions":{"view_html":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F","download_json":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F.json","view_paper":"https://pith.science/paper/NTNGR354","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.10204&json=true","fetch_graph":"https://pith.science/api/pith-number/NTNGR354EERZO4CJBDE7CBJI3F/graph.json","fetch_events":"https://pith.science/api/pith-number/NTNGR354EERZO4CJBDE7CBJI3F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F/action/storage_attestation","attest_author":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F/action/author_attestation","sign_citation":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F/action/citation_signature","submit_replication":"https://pith.science/pith/NTNGR354EERZO4CJBDE7CBJI3F/action/replication_record"}},"created_at":"2026-05-18T00:14:57.973568+00:00","updated_at":"2026-05-18T00:14:57.973568+00:00"}