{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BIVBPRXU4ADM2SKIQ334CQCLRL","short_pith_number":"pith:BIVBPRXU","canonical_record":{"source":{"id":"2410.02195","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T04:16:49Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"72cb0693d4346e986f498d5122d26c1e7119bd90a68813c13be7f421bc19f185","abstract_canon_sha256":"171c8f8cafc95062fa584566e9a7b3ef12e10fe3803e7194020a4b5db1c1b474"},"schema_version":"1.0"},"canonical_sha256":"0a2a17c6f4e006cd494886f7c1404b8adf91598fd31b4007d3a0436237815b7b","source":{"kind":"arxiv","id":"2410.02195","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02195","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02195v1","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02195","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"BIVBPRXU4ADM","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"BIVBPRXU4ADM2SKI","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"BIVBPRXU","created_at":"2026-07-05T09:15:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BIVBPRXU4ADM2SKIQ334CQCLRL","target":"record","payload":{"canonical_record":{"source":{"id":"2410.02195","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T04:16:49Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"72cb0693d4346e986f498d5122d26c1e7119bd90a68813c13be7f421bc19f185","abstract_canon_sha256":"171c8f8cafc95062fa584566e9a7b3ef12e10fe3803e7194020a4b5db1c1b474"},"schema_version":"1.0"},"canonical_sha256":"0a2a17c6f4e006cd494886f7c1404b8adf91598fd31b4007d3a0436237815b7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:17.478381Z","signature_b64":"EQ9dsZETTaqANYfGvVQp/fH6TvyEoFn0ezWN0sNlksKrNAoF16OY8+NP4PveLQM9I+50LaDyAy0y1pgMfz6PBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a2a17c6f4e006cd494886f7c1404b8adf91598fd31b4007d3a0436237815b7b","last_reissued_at":"2026-07-05T09:15:17.477876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:17.477876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.02195","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":"2026-07-05T09:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OkWa9ZbkSkrLwQ+Ja7xiyoHomNeiR5BBOcjcaxlX5GSEztVThikajhpgwCIY690/V1Lq099S2i2yfNE1hBOLAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:21:11.156137Z"},"content_sha256":"2c39ed7510ba4275dd1a3d90d74b7d45dcb30c336d080b4445d551bb9064a1df","schema_version":"1.0","event_id":"sha256:2c39ed7510ba4275dd1a3d90d74b7d45dcb30c336d080b4445d551bb9064a1df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BIVBPRXU4ADM2SKIQ334CQCLRL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Dongqi Fu, Hanghang Tong, Ruizhong Qiu, Xiao Lin, Zhining Liu","submitted_at":"2024-10-03T04:16:49Z","abstract_excerpt":"Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works have explored the robustness of MTS forecasting models to malicious attacks, which is crucial for their trustworthy employment in high-stake scenarios. To address this gap, we dive deep into the backdoor attacks on MTS forecasting models and propose an effective attack method named BackTime.By subtly injecting a few stealthy triggers into the MTS dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02195","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/2410.02195/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":"2026-07-05T09:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4+eizfE07aD+I1jnihY1/asAbOJaYlLnbIeSKLy2EYcfrFuFqY7D4L4fSvXGNaMaVWyGtJ/QG/CkCsnLTFVYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:21:11.156651Z"},"content_sha256":"b7b05b6654f6917140a4635c87551d921b8a8d5c2907beca17f28190ec35c79a","schema_version":"1.0","event_id":"sha256:b7b05b6654f6917140a4635c87551d921b8a8d5c2907beca17f28190ec35c79a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/bundle.json","state_url":"https://pith.science/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/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":"2026-08-08T22:21:11Z","links":{"resolver":"https://pith.science/pith/BIVBPRXU4ADM2SKIQ334CQCLRL","bundle":"https://pith.science/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/bundle.json","state":"https://pith.science/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BIVBPRXU4ADM2SKIQ334CQCLRL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BIVBPRXU4ADM2SKIQ334CQCLRL","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":"171c8f8cafc95062fa584566e9a7b3ef12e10fe3803e7194020a4b5db1c1b474","cross_cats_sorted":["cs.AI","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T04:16:49Z","title_canon_sha256":"72cb0693d4346e986f498d5122d26c1e7119bd90a68813c13be7f421bc19f185"},"schema_version":"1.0","source":{"id":"2410.02195","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02195","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02195v1","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02195","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"BIVBPRXU4ADM","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"BIVBPRXU4ADM2SKI","created_at":"2026-07-05T09:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"BIVBPRXU","created_at":"2026-07-05T09:15:17Z"}],"graph_snapshots":[{"event_id":"sha256:b7b05b6654f6917140a4635c87551d921b8a8d5c2907beca17f28190ec35c79a","target":"graph","created_at":"2026-07-05T09:15:17Z","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/2410.02195/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works have explored the robustness of MTS forecasting models to malicious attacks, which is crucial for their trustworthy employment in high-stake scenarios. To address this gap, we dive deep into the backdoor attacks on MTS forecasting models and propose an effective attack method named BackTime.By subtly injecting a few stealthy triggers into the MTS dat","authors_text":"Dongqi Fu, Hanghang Tong, Ruizhong Qiu, Xiao Lin, Zhining Liu","cross_cats":["cs.AI","cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T04:16:49Z","title":"BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02195","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":"sha256:2c39ed7510ba4275dd1a3d90d74b7d45dcb30c336d080b4445d551bb9064a1df","target":"record","created_at":"2026-07-05T09:15:17Z","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":"171c8f8cafc95062fa584566e9a7b3ef12e10fe3803e7194020a4b5db1c1b474","cross_cats_sorted":["cs.AI","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T04:16:49Z","title_canon_sha256":"72cb0693d4346e986f498d5122d26c1e7119bd90a68813c13be7f421bc19f185"},"schema_version":"1.0","source":{"id":"2410.02195","kind":"arxiv","version":1}},"canonical_sha256":"0a2a17c6f4e006cd494886f7c1404b8adf91598fd31b4007d3a0436237815b7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a2a17c6f4e006cd494886f7c1404b8adf91598fd31b4007d3a0436237815b7b","first_computed_at":"2026-07-05T09:15:17.477876Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:17.477876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EQ9dsZETTaqANYfGvVQp/fH6TvyEoFn0ezWN0sNlksKrNAoF16OY8+NP4PveLQM9I+50LaDyAy0y1pgMfz6PBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:17.478381Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.02195","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c39ed7510ba4275dd1a3d90d74b7d45dcb30c336d080b4445d551bb9064a1df","sha256:b7b05b6654f6917140a4635c87551d921b8a8d5c2907beca17f28190ec35c79a"],"state_sha256":"ba409e224e13794de07491b71ca2a932c65d6c3cd29baed32bfd10befd18ae76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ycIVVyIe7z5DJ8CphncpTQWp2zdPmqwV4GRdQY+DdJNbhXgd/tjt5zbN/W27EqfsUyTRIfBrEHKBfwVAtwKZCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:21:11.161138Z","bundle_sha256":"d2a550b3cfd1d0be6b90d2d0f21216872a0186fdfcdc5dc4d3cac5b25727a3bc"}}