{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:EN43VRYUZSSITPQKAMVEA5MWZC","short_pith_number":"pith:EN43VRYU","schema_version":"1.0","canonical_sha256":"2379bac714cca489be0a032a407596c880f2827fd795b912275f046dcee52b32","source":{"kind":"arxiv","id":"2008.12454","version":2},"attestation_state":"computed","paper":{"title":"Color and Edge-Aware Adversarial Image Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Mitchell Graves, Patrick Reilly, Robert Bassett","submitted_at":"2020-08-28T03:02:20Z","abstract_excerpt":"Adversarial perturbation of images, in which a source image is deliberately modified with the intent of causing a classifier to misclassify the image, provides important insight into the robustness of image classifiers. In this work we develop two new methods for constructing adversarial perturbations, both of which are motivated by minimizing human ability to detect changes between the perturbed and source image. The first of these, the Edge-Aware method, reduces the magnitude of perturbations permitted in smooth regions of an image where changes are more easily detected. Our second method, t"},"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":"2008.12454","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-28T03:02:20Z","cross_cats_sorted":["eess.IV","stat.ML"],"title_canon_sha256":"b707748efcbe88e465f657340c47e4cbc48887974bfdc2f64e737b099cb06b91","abstract_canon_sha256":"841eb5f3ff5354938b14b928b541480418c504a0e2ae88b5f935e9c3d8ee1ca4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:25:22.929135Z","signature_b64":"j8Kz+GcO+DfF4zCH4XqJ1fkcCJuFyGw9Sx5nd0OeV+IPs2XsTXirZtqhyumjnTiGICO/0VwCK7oWHB1wJNafDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2379bac714cca489be0a032a407596c880f2827fd795b912275f046dcee52b32","last_reissued_at":"2026-07-05T02:25:22.928640Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:25:22.928640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Color and Edge-Aware Adversarial Image Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Mitchell Graves, Patrick Reilly, Robert Bassett","submitted_at":"2020-08-28T03:02:20Z","abstract_excerpt":"Adversarial perturbation of images, in which a source image is deliberately modified with the intent of causing a classifier to misclassify the image, provides important insight into the robustness of image classifiers. In this work we develop two new methods for constructing adversarial perturbations, both of which are motivated by minimizing human ability to detect changes between the perturbed and source image. The first of these, the Edge-Aware method, reduces the magnitude of perturbations permitted in smooth regions of an image where changes are more easily detected. Our second method, t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.12454","kind":"arxiv","version":2},"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/2008.12454/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":"2008.12454","created_at":"2026-07-05T02:25:22.928699+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.12454v2","created_at":"2026-07-05T02:25:22.928699+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.12454","created_at":"2026-07-05T02:25:22.928699+00:00"},{"alias_kind":"pith_short_12","alias_value":"EN43VRYUZSSI","created_at":"2026-07-05T02:25:22.928699+00:00"},{"alias_kind":"pith_short_16","alias_value":"EN43VRYUZSSITPQK","created_at":"2026-07-05T02:25:22.928699+00:00"},{"alias_kind":"pith_short_8","alias_value":"EN43VRYU","created_at":"2026-07-05T02:25:22.928699+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.16313","citing_title":"Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC","json":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC.json","graph_json":"https://pith.science/api/pith-number/EN43VRYUZSSITPQKAMVEA5MWZC/graph.json","events_json":"https://pith.science/api/pith-number/EN43VRYUZSSITPQKAMVEA5MWZC/events.json","paper":"https://pith.science/paper/EN43VRYU"},"agent_actions":{"view_html":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC","download_json":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC.json","view_paper":"https://pith.science/paper/EN43VRYU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.12454&json=true","fetch_graph":"https://pith.science/api/pith-number/EN43VRYUZSSITPQKAMVEA5MWZC/graph.json","fetch_events":"https://pith.science/api/pith-number/EN43VRYUZSSITPQKAMVEA5MWZC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC/action/storage_attestation","attest_author":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC/action/author_attestation","sign_citation":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC/action/citation_signature","submit_replication":"https://pith.science/pith/EN43VRYUZSSITPQKAMVEA5MWZC/action/replication_record"}},"created_at":"2026-07-05T02:25:22.928699+00:00","updated_at":"2026-07-05T02:25:22.928699+00:00"}