{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LAII6K4DTX33AY54OHJBC7FPR2","short_pith_number":"pith:LAII6K4D","schema_version":"1.0","canonical_sha256":"58108f2b839df7b063bc71d2117caf8ea220c4005865f5be6ed26b09c43abdb9","source":{"kind":"arxiv","id":"1704.05712","version":3},"attestation_state":"computed","paper":{"title":"Universal Adversarial Perturbations Against Semantic Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, Volker Fischer","submitted_at":"2017-04-19T12:48:52Z","abstract_excerpt":"While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial "},"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":"1704.05712","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-19T12:48:52Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"ac6304eb30f3b8ae325938c1b84410edb0ed3187e6b595793d71679c52f87b02","abstract_canon_sha256":"ef7012ccc4cdb02ca2e439d2c0238c52cf33fc584d6fdafd0305c6f8713c8e4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:06.591370Z","signature_b64":"Hqg7Ef4rYer2GXc+6Cq1zJ2o+VoQVp7uN2rowy1UaEZknnIL/3h+0bVWAIYfExOIRdQ0MM1BwfwUOQ3NP2zXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58108f2b839df7b063bc71d2117caf8ea220c4005865f5be6ed26b09c43abdb9","last_reissued_at":"2026-05-18T00:39:06.590681Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:06.590681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Adversarial Perturbations Against Semantic Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, Volker Fischer","submitted_at":"2017-04-19T12:48:52Z","abstract_excerpt":"While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.05712","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":""},"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":"1704.05712","created_at":"2026-05-18T00:39:06.590780+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.05712v3","created_at":"2026-05-18T00:39:06.590780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.05712","created_at":"2026-05-18T00:39:06.590780+00:00"},{"alias_kind":"pith_short_12","alias_value":"LAII6K4DTX33","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LAII6K4DTX33AY54","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LAII6K4D","created_at":"2026-05-18T12:31:28.150371+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/LAII6K4DTX33AY54OHJBC7FPR2","json":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2.json","graph_json":"https://pith.science/api/pith-number/LAII6K4DTX33AY54OHJBC7FPR2/graph.json","events_json":"https://pith.science/api/pith-number/LAII6K4DTX33AY54OHJBC7FPR2/events.json","paper":"https://pith.science/paper/LAII6K4D"},"agent_actions":{"view_html":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2","download_json":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2.json","view_paper":"https://pith.science/paper/LAII6K4D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.05712&json=true","fetch_graph":"https://pith.science/api/pith-number/LAII6K4DTX33AY54OHJBC7FPR2/graph.json","fetch_events":"https://pith.science/api/pith-number/LAII6K4DTX33AY54OHJBC7FPR2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2/action/storage_attestation","attest_author":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2/action/author_attestation","sign_citation":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2/action/citation_signature","submit_replication":"https://pith.science/pith/LAII6K4DTX33AY54OHJBC7FPR2/action/replication_record"}},"created_at":"2026-05-18T00:39:06.590780+00:00","updated_at":"2026-05-18T00:39:06.590780+00:00"}