{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:N5YFJR4ZFYZG24PZXFM4UKLTDV","short_pith_number":"pith:N5YFJR4Z","canonical_record":{"source":{"id":"1809.05962","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T21:49:14Z","cross_cats_sorted":[],"title_canon_sha256":"ef61057325aa706b4ba71c47ea3748687807dbb183c9b73bce84f436fb927860","abstract_canon_sha256":"54102656f32c85556d43bcc941416808b728d585c6a6bc4c877ade19818c848b"},"schema_version":"1.0"},"canonical_sha256":"6f7054c7992e326d71f9b959ca29731d4104151028c87e7c5c5ae8e31810839c","source":{"kind":"arxiv","id":"1809.05962","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05962","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05962v2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05962","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"N5YFJR4ZFYZG","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"N5YFJR4ZFYZG24PZ","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"N5YFJR4Z","created_at":"2026-07-05T00:16:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:N5YFJR4ZFYZG24PZXFM4UKLTDV","target":"record","payload":{"canonical_record":{"source":{"id":"1809.05962","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T21:49:14Z","cross_cats_sorted":[],"title_canon_sha256":"ef61057325aa706b4ba71c47ea3748687807dbb183c9b73bce84f436fb927860","abstract_canon_sha256":"54102656f32c85556d43bcc941416808b728d585c6a6bc4c877ade19818c848b"},"schema_version":"1.0"},"canonical_sha256":"6f7054c7992e326d71f9b959ca29731d4104151028c87e7c5c5ae8e31810839c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:16:31.267101Z","signature_b64":"o8McFcY6qrse3+8HZy2emJDd6IImieADxEh9kBf4zI4ao1onVR4dyPFTPXlUuxQi0mhVDHp5IOssauIG5uJiAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f7054c7992e326d71f9b959ca29731d4104151028c87e7c5c5ae8e31810839c","last_reissued_at":"2026-07-05T00:16:31.266646Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:16:31.266646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.05962","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L2y9WLxYIc5EUTgBINZ2Yl5splAup3PuYvZ+Vs4Fbx0c60hyRD8Ljv1YsF9IVFosJRDInda0LL5IbIAJ7OBwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:05:37.626541Z"},"content_sha256":"44331f18e031f7011fe2091b24d66fd7d7e6ea1f23eaec3998fb55df36de94fc","schema_version":"1.0","event_id":"sha256:44331f18e031f7011fe2091b24d66fd7d7e6ea1f23eaec3998fb55df36de94fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:N5YFJR4ZFYZG24PZXFM4UKLTDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Adversarial Perturbation on Deep Proposal-based Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Tian, Ming-Ching Chang, Siwei Lyu, Xiao Bian, Yuezun Li","submitted_at":"2018-09-16T21:49:14Z","abstract_excerpt":"Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05962","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/1809.05962/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s+1z+ptsDkp64n8adKjnvmXLkLsXQlxeZ77yC5spnomXw7a6KdptcJ29Hglhm8QDnD69/pE5QOieWaMVo4r/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:05:37.627042Z"},"content_sha256":"9e18b9c812f81b5c4919d62c859a95e8b32c2c66c65a47912694fadc3ae914cc","schema_version":"1.0","event_id":"sha256:9e18b9c812f81b5c4919d62c859a95e8b32c2c66c65a47912694fadc3ae914cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/bundle.json","state_url":"https://pith.science/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T07:05:37Z","links":{"resolver":"https://pith.science/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV","bundle":"https://pith.science/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/bundle.json","state":"https://pith.science/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N5YFJR4ZFYZG24PZXFM4UKLTDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:N5YFJR4ZFYZG24PZXFM4UKLTDV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"54102656f32c85556d43bcc941416808b728d585c6a6bc4c877ade19818c848b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T21:49:14Z","title_canon_sha256":"ef61057325aa706b4ba71c47ea3748687807dbb183c9b73bce84f436fb927860"},"schema_version":"1.0","source":{"id":"1809.05962","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05962","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05962v2","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05962","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"N5YFJR4ZFYZG","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"N5YFJR4ZFYZG24PZ","created_at":"2026-07-05T00:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"N5YFJR4Z","created_at":"2026-07-05T00:16:31Z"}],"graph_snapshots":[{"event_id":"sha256:9e18b9c812f81b5c4919d62c859a95e8b32c2c66c65a47912694fadc3ae914cc","target":"graph","created_at":"2026-07-05T00:16:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1809.05962/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterat","authors_text":"Daniel Tian, Ming-Ching Chang, Siwei Lyu, Xiao Bian, Yuezun Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T21:49:14Z","title":"Robust Adversarial Perturbation on Deep Proposal-based Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05962","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:44331f18e031f7011fe2091b24d66fd7d7e6ea1f23eaec3998fb55df36de94fc","target":"record","created_at":"2026-07-05T00:16:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"54102656f32c85556d43bcc941416808b728d585c6a6bc4c877ade19818c848b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T21:49:14Z","title_canon_sha256":"ef61057325aa706b4ba71c47ea3748687807dbb183c9b73bce84f436fb927860"},"schema_version":"1.0","source":{"id":"1809.05962","kind":"arxiv","version":2}},"canonical_sha256":"6f7054c7992e326d71f9b959ca29731d4104151028c87e7c5c5ae8e31810839c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f7054c7992e326d71f9b959ca29731d4104151028c87e7c5c5ae8e31810839c","first_computed_at":"2026-07-05T00:16:31.266646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:16:31.266646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o8McFcY6qrse3+8HZy2emJDd6IImieADxEh9kBf4zI4ao1onVR4dyPFTPXlUuxQi0mhVDHp5IOssauIG5uJiAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:16:31.267101Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.05962","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44331f18e031f7011fe2091b24d66fd7d7e6ea1f23eaec3998fb55df36de94fc","sha256:9e18b9c812f81b5c4919d62c859a95e8b32c2c66c65a47912694fadc3ae914cc"],"state_sha256":"d0a2bd9b0de3fed5c698f703fee41e6d7d8691b346e18e50554b6ddb3fd94b13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FGfvSim3Ns1ESTRaZJ70Cdlk02UF3MOt3cQIgBWHOlQUzX3k9rEVHOnQerIhaMr9o/KNPUAQ7ILCmxyC4E3jBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T07:05:37.630540Z","bundle_sha256":"672fc9f6b1489040658c1c0f1d1de1858c606db2c2e3d79ee43fc1c1d898bfe6"}}