{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:R56APAC5LPP5JGAOCIWQSODH6W","short_pith_number":"pith:R56APAC5","schema_version":"1.0","canonical_sha256":"8f7c07805d5bdfd4980e122d093867f5a86af0cc67c2745e22583c8992d4dde2","source":{"kind":"arxiv","id":"2504.21226","version":3},"attestation_state":"computed","paper":{"title":"MemeBLIP2: A novel lightweight multimodal system to detect harmful memes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aowei Shen, Changlin Yang, Jiaqi Liu, Lisha Xu, Ran Tong, Shuzheng Li","submitted_at":"2025-04-29T23:41:06Z","abstract_excerpt":"Memes often merge visuals with brief text to share humor or opinions, yet some memes contain harmful messages such as hate speech. In this paper, we introduces MemeBLIP2, a light weight multimodal system that detects harmful memes by combining image and text features effectively. We build on previous studies by adding modules that align image and text representations into a shared space and fuse them for better classification. Using BLIP-2 as the core vision-language model, our system is evaluated on the PrideMM datasets. The results show that MemeBLIP2 can capture subtle cues in both modaliti"},"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":"2504.21226","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T23:41:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab2a38b1093727c971fea8ff60a3dace56f3e64f3f4c1c77521f537c38b6e2cc","abstract_canon_sha256":"8eba6fe9aab7e9f47442c3d1d078dae410db249f2da5ae6d30e1446658183dfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:05.064602Z","signature_b64":"5YGhAA9ZAnaWAe06pszvsZ5xs6VUKvE+PQg22mr69rrIKL6B/BdPAIU3m1BBY2WZDfFhzwMgWKI6X1rLc8GiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f7c07805d5bdfd4980e122d093867f5a86af0cc67c2745e22583c8992d4dde2","last_reissued_at":"2026-07-05T11:44:05.064058Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:05.064058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MemeBLIP2: A novel lightweight multimodal system to detect harmful memes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aowei Shen, Changlin Yang, Jiaqi Liu, Lisha Xu, Ran Tong, Shuzheng Li","submitted_at":"2025-04-29T23:41:06Z","abstract_excerpt":"Memes often merge visuals with brief text to share humor or opinions, yet some memes contain harmful messages such as hate speech. In this paper, we introduces MemeBLIP2, a light weight multimodal system that detects harmful memes by combining image and text features effectively. We build on previous studies by adding modules that align image and text representations into a shared space and fuse them for better classification. Using BLIP-2 as the core vision-language model, our system is evaluated on the PrideMM datasets. The results show that MemeBLIP2 can capture subtle cues in both modaliti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.21226","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.21226/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":"2504.21226","created_at":"2026-07-05T11:44:05.064120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.21226v3","created_at":"2026-07-05T11:44:05.064120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.21226","created_at":"2026-07-05T11:44:05.064120+00:00"},{"alias_kind":"pith_short_12","alias_value":"R56APAC5LPP5","created_at":"2026-07-05T11:44:05.064120+00:00"},{"alias_kind":"pith_short_16","alias_value":"R56APAC5LPP5JGAO","created_at":"2026-07-05T11:44:05.064120+00:00"},{"alias_kind":"pith_short_8","alias_value":"R56APAC5","created_at":"2026-07-05T11:44:05.064120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.11743","citing_title":"Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W","json":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W.json","graph_json":"https://pith.science/api/pith-number/R56APAC5LPP5JGAOCIWQSODH6W/graph.json","events_json":"https://pith.science/api/pith-number/R56APAC5LPP5JGAOCIWQSODH6W/events.json","paper":"https://pith.science/paper/R56APAC5"},"agent_actions":{"view_html":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W","download_json":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W.json","view_paper":"https://pith.science/paper/R56APAC5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.21226&json=true","fetch_graph":"https://pith.science/api/pith-number/R56APAC5LPP5JGAOCIWQSODH6W/graph.json","fetch_events":"https://pith.science/api/pith-number/R56APAC5LPP5JGAOCIWQSODH6W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W/action/storage_attestation","attest_author":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W/action/author_attestation","sign_citation":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W/action/citation_signature","submit_replication":"https://pith.science/pith/R56APAC5LPP5JGAOCIWQSODH6W/action/replication_record"}},"created_at":"2026-07-05T11:44:05.064120+00:00","updated_at":"2026-07-05T11:44:05.064120+00:00"}