{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BOJ2Q4DECXKSLV7UVPTRMAJ3KJ","short_pith_number":"pith:BOJ2Q4DE","schema_version":"1.0","canonical_sha256":"0b93a8706415d525d7f4abe716013b525346cb9056f37c54a72325e2c4fc9c24","source":{"kind":"arxiv","id":"2404.15744","version":2},"attestation_state":"computed","paper":{"title":"A General Black-box Adversarial Attack on Graph-based Fake News Detectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Jiwei Tian, Keke Tang, Peican Zhu, Yang Liu, Zechen Pan, Zhen Wang","submitted_at":"2024-04-24T09:04:05Z","abstract_excerpt":"Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interact"},"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":"2404.15744","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-24T09:04:05Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"71ae67fc45e967912adb945dc5be3982775239b80469610c14e585a9336d71fb","abstract_canon_sha256":"941a95fa1d7488144a74f9cb35ecba669d25f1ed86b3f0e01d7f38346552169e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:32.348290Z","signature_b64":"GrSyOc4i0uCdFh4okZhKaLGsmK51WOSuF926gFxCnxaeuJ1+Qqq4U0DPJcBmbF5MwXO+iUs9NSgimWUe+wkcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b93a8706415d525d7f4abe716013b525346cb9056f37c54a72325e2c4fc9c24","last_reissued_at":"2026-07-05T08:12:32.347791Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:32.347791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A General Black-box Adversarial Attack on Graph-based Fake News Detectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Jiwei Tian, Keke Tang, Peican Zhu, Yang Liu, Zechen Pan, Zhen Wang","submitted_at":"2024-04-24T09:04:05Z","abstract_excerpt":"Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interact"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.15744","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/2404.15744/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":"2404.15744","created_at":"2026-07-05T08:12:32.347848+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.15744v2","created_at":"2026-07-05T08:12:32.347848+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.15744","created_at":"2026-07-05T08:12:32.347848+00:00"},{"alias_kind":"pith_short_12","alias_value":"BOJ2Q4DECXKS","created_at":"2026-07-05T08:12:32.347848+00:00"},{"alias_kind":"pith_short_16","alias_value":"BOJ2Q4DECXKSLV7U","created_at":"2026-07-05T08:12:32.347848+00:00"},{"alias_kind":"pith_short_8","alias_value":"BOJ2Q4DE","created_at":"2026-07-05T08:12:32.347848+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.14453","citing_title":"Robustness Evaluation of Graph-based News Detection Using Network Structural Information","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ","json":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ.json","graph_json":"https://pith.science/api/pith-number/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/graph.json","events_json":"https://pith.science/api/pith-number/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/events.json","paper":"https://pith.science/paper/BOJ2Q4DE"},"agent_actions":{"view_html":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ","download_json":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ.json","view_paper":"https://pith.science/paper/BOJ2Q4DE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.15744&json=true","fetch_graph":"https://pith.science/api/pith-number/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/graph.json","fetch_events":"https://pith.science/api/pith-number/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/action/storage_attestation","attest_author":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/action/author_attestation","sign_citation":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/action/citation_signature","submit_replication":"https://pith.science/pith/BOJ2Q4DECXKSLV7UVPTRMAJ3KJ/action/replication_record"}},"created_at":"2026-07-05T08:12:32.347848+00:00","updated_at":"2026-07-05T08:12:32.347848+00:00"}