{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:NYXAGJBZ5MVCLOXTOVO7MV5QOE","short_pith_number":"pith:NYXAGJBZ","canonical_record":{"source":{"id":"2310.10744","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T18:20:44Z","cross_cats_sorted":[],"title_canon_sha256":"c6f9cb7e3b85abde455a7233dadc1bdc72f997e43eb92ec7c086ca43c4879365","abstract_canon_sha256":"5f4a22b4256d4fc79a19a8e1b4044cc90b0c9de4be553ddd98625f100dc0c025"},"schema_version":"1.0"},"canonical_sha256":"6e2e032439eb2a25baf3755df657b0710240cb39db3903fe3b84e97153d91175","source":{"kind":"arxiv","id":"2310.10744","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10744","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10744v1","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10744","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_12","alias_value":"NYXAGJBZ5MVC","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_16","alias_value":"NYXAGJBZ5MVCLOXT","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_8","alias_value":"NYXAGJBZ","created_at":"2026-07-05T07:01:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:NYXAGJBZ5MVCLOXTOVO7MV5QOE","target":"record","payload":{"canonical_record":{"source":{"id":"2310.10744","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T18:20:44Z","cross_cats_sorted":[],"title_canon_sha256":"c6f9cb7e3b85abde455a7233dadc1bdc72f997e43eb92ec7c086ca43c4879365","abstract_canon_sha256":"5f4a22b4256d4fc79a19a8e1b4044cc90b0c9de4be553ddd98625f100dc0c025"},"schema_version":"1.0"},"canonical_sha256":"6e2e032439eb2a25baf3755df657b0710240cb39db3903fe3b84e97153d91175","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:27.238183Z","signature_b64":"2/nhQbLpBViHb88MpHykfPN8bf79lFCA9clABgcZJTs+OnPGBbWv39mbuCOujSunaZNT24EaOhpekxM52Nx2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e2e032439eb2a25baf3755df657b0710240cb39db3903fe3b84e97153d91175","last_reissued_at":"2026-07-05T07:01:27.237708Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:27.237708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.10744","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:01:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Ev1J39TKhBTFd5LiT4HGg68GrxcjeGN28bOJhA4EwpAU1/MNdyLIBfC0/96g8E0Dvw6eXtd+9oYa5rCDonQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T11:46:39.544192Z"},"content_sha256":"808e03b5b28afc424bef319828f8f14df3616c0dfe78536c1da97673d9eb9cee","schema_version":"1.0","event_id":"sha256:808e03b5b28afc424bef319828f8f14df3616c0dfe78536c1da97673d9eb9cee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:NYXAGJBZ5MVCLOXTOVO7MV5QOE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Adversarial Label-Flipping Attack on Tabular Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gillian Dobbie, J\\\"org Wicker, Xinglong Chang","submitted_at":"2023-10-16T18:20:44Z","abstract_excerpt":"Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which the adversarial label-flipping attack poses significant threats. In label-flipping attacks, the adversary maliciously flips a portion of training labels to compromise the machine learning model. This paper raises significant concerns as these attacks can camouflage a highly skewed dataset as an easily solvable classification problem, often misleading machine learning practitioners into lower defenses and miscalculat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10744","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.10744/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":"2026-07-05T07:01:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rcFWm+hdhvPORjRmAhKOORwL1m4GPfml6MMZeB4R4RYk47+xYjrW1NSyJikkFUU7rHDrz+Mkois00ejoSMiIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T11:46:39.544571Z"},"content_sha256":"1df6740c74c625f9a05d617bd5246cae3709d6799a0fd254e7ff0c698bf2c83a","schema_version":"1.0","event_id":"sha256:1df6740c74c625f9a05d617bd5246cae3709d6799a0fd254e7ff0c698bf2c83a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/bundle.json","state_url":"https://pith.science/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/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":"2026-08-20T11:46:39Z","links":{"resolver":"https://pith.science/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE","bundle":"https://pith.science/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/bundle.json","state":"https://pith.science/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYXAGJBZ5MVCLOXTOVO7MV5QOE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NYXAGJBZ5MVCLOXTOVO7MV5QOE","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":"5f4a22b4256d4fc79a19a8e1b4044cc90b0c9de4be553ddd98625f100dc0c025","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T18:20:44Z","title_canon_sha256":"c6f9cb7e3b85abde455a7233dadc1bdc72f997e43eb92ec7c086ca43c4879365"},"schema_version":"1.0","source":{"id":"2310.10744","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10744","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10744v1","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10744","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_12","alias_value":"NYXAGJBZ5MVC","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_16","alias_value":"NYXAGJBZ5MVCLOXT","created_at":"2026-07-05T07:01:27Z"},{"alias_kind":"pith_short_8","alias_value":"NYXAGJBZ","created_at":"2026-07-05T07:01:27Z"}],"graph_snapshots":[{"event_id":"sha256:1df6740c74c625f9a05d617bd5246cae3709d6799a0fd254e7ff0c698bf2c83a","target":"graph","created_at":"2026-07-05T07:01:27Z","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/2310.10744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which the adversarial label-flipping attack poses significant threats. In label-flipping attacks, the adversary maliciously flips a portion of training labels to compromise the machine learning model. This paper raises significant concerns as these attacks can camouflage a highly skewed dataset as an easily solvable classification problem, often misleading machine learning practitioners into lower defenses and miscalculat","authors_text":"Gillian Dobbie, J\\\"org Wicker, Xinglong Chang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T18:20:44Z","title":"Fast Adversarial Label-Flipping Attack on Tabular Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10744","kind":"arxiv","version":1},"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":"sha256:808e03b5b28afc424bef319828f8f14df3616c0dfe78536c1da97673d9eb9cee","target":"record","created_at":"2026-07-05T07:01:27Z","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":"5f4a22b4256d4fc79a19a8e1b4044cc90b0c9de4be553ddd98625f100dc0c025","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T18:20:44Z","title_canon_sha256":"c6f9cb7e3b85abde455a7233dadc1bdc72f997e43eb92ec7c086ca43c4879365"},"schema_version":"1.0","source":{"id":"2310.10744","kind":"arxiv","version":1}},"canonical_sha256":"6e2e032439eb2a25baf3755df657b0710240cb39db3903fe3b84e97153d91175","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e2e032439eb2a25baf3755df657b0710240cb39db3903fe3b84e97153d91175","first_computed_at":"2026-07-05T07:01:27.237708Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:27.237708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2/nhQbLpBViHb88MpHykfPN8bf79lFCA9clABgcZJTs+OnPGBbWv39mbuCOujSunaZNT24EaOhpekxM52Nx2DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:27.238183Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10744","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:808e03b5b28afc424bef319828f8f14df3616c0dfe78536c1da97673d9eb9cee","sha256:1df6740c74c625f9a05d617bd5246cae3709d6799a0fd254e7ff0c698bf2c83a"],"state_sha256":"f016acaff3234cd7636d5f93e956ede85defec6b214ad33525166a3d7de48838"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PiZdQyLq00rKPBVyeT3ipMLu5SE/xQkhILGqDUAdbwoUUo5k6PS3TTueq/k/CgDcyh495ssMXCTTyvq1QrbXCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T11:46:39.547273Z","bundle_sha256":"98537e99a040e77ed1e8d71310cdaf422b91ddb869344ee704054364ca6ba3e2"}}