{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:K37FPNHRTDRQEG33G633QISYEI","short_pith_number":"pith:K37FPNHR","schema_version":"1.0","canonical_sha256":"56fe57b4f198e3021b7b37b7b82258221d07f15ce32a5b47bb51fc7ce5877676","source":{"kind":"arxiv","id":"1908.08016","version":4},"attestation_state":"computed","paper":{"title":"Testing Robustness Against Unforeseen Adversaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Adam Dziedzic, Akul Arora, Dan Hendrycks, Daniel Kang, Franziska Boenisch, Jacob Steinhardt, Mantas Mazeika, Max Kaufmann, Steven Basart, Tom Brown, Xuwang Yin, Yi Sun","submitted_at":"2019-08-21T17:36:48Z","abstract_excerpt":"Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-world applications developers are unlikely to have access to the full range of attacks or corruptions their system will face. Furthermore, worst-case inputs are likely to be diverse and need not be constrained to the L_p ball. To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen ne"},"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":"1908.08016","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-21T17:36:48Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"d282757c09bb283fd69383570d7722578fc7a673751ca47ec8e98cab817c7a6d","abstract_canon_sha256":"edaf567d7ce651568784c5cea070a41547749822dadf244ea7903014f523aeff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:16.180680Z","signature_b64":"Lk85W1FraGzumXOPBH/0R1Uaek3x+M69n6FcRrhVN0dyC/+0yjbNIJxbKal03z5mH5ThnYc11qjX1yw5y29rAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56fe57b4f198e3021b7b37b7b82258221d07f15ce32a5b47bb51fc7ce5877676","last_reissued_at":"2026-07-05T07:06:16.180183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:16.180183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Testing Robustness Against Unforeseen Adversaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Adam Dziedzic, Akul Arora, Dan Hendrycks, Daniel Kang, Franziska Boenisch, Jacob Steinhardt, Mantas Mazeika, Max Kaufmann, Steven Basart, Tom Brown, Xuwang Yin, Yi Sun","submitted_at":"2019-08-21T17:36:48Z","abstract_excerpt":"Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-world applications developers are unlikely to have access to the full range of attacks or corruptions their system will face. Furthermore, worst-case inputs are likely to be diverse and need not be constrained to the L_p ball. To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08016","kind":"arxiv","version":4},"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/1908.08016/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":"1908.08016","created_at":"2026-07-05T07:06:16.180242+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.08016v4","created_at":"2026-07-05T07:06:16.180242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08016","created_at":"2026-07-05T07:06:16.180242+00:00"},{"alias_kind":"pith_short_12","alias_value":"K37FPNHRTDRQ","created_at":"2026-07-05T07:06:16.180242+00:00"},{"alias_kind":"pith_short_16","alias_value":"K37FPNHRTDRQEG33","created_at":"2026-07-05T07:06:16.180242+00:00"},{"alias_kind":"pith_short_8","alias_value":"K37FPNHR","created_at":"2026-07-05T07:06:16.180242+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.16950","citing_title":"LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI","json":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI.json","graph_json":"https://pith.science/api/pith-number/K37FPNHRTDRQEG33G633QISYEI/graph.json","events_json":"https://pith.science/api/pith-number/K37FPNHRTDRQEG33G633QISYEI/events.json","paper":"https://pith.science/paper/K37FPNHR"},"agent_actions":{"view_html":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI","download_json":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI.json","view_paper":"https://pith.science/paper/K37FPNHR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.08016&json=true","fetch_graph":"https://pith.science/api/pith-number/K37FPNHRTDRQEG33G633QISYEI/graph.json","fetch_events":"https://pith.science/api/pith-number/K37FPNHRTDRQEG33G633QISYEI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI/action/storage_attestation","attest_author":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI/action/author_attestation","sign_citation":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI/action/citation_signature","submit_replication":"https://pith.science/pith/K37FPNHRTDRQEG33G633QISYEI/action/replication_record"}},"created_at":"2026-07-05T07:06:16.180242+00:00","updated_at":"2026-07-05T07:06:16.180242+00:00"}