{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:36LAZHUZH53RMDARXYEKCSNDPV","short_pith_number":"pith:36LAZHUZ","canonical_record":{"source":{"id":"2307.08327","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-17T08:50:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9e098d1b29b152a30b5809b3e063870903fa3559ef993f691efb793ee60ffb3e","abstract_canon_sha256":"e32acd1f6d0697995cf76b80a398ddd358a553ecc82e57ae2570818555cb73d3"},"schema_version":"1.0"},"canonical_sha256":"df960c9e993f77160c11be08a149a37d7c58dcee53deca8ac39a225a040b87ff","source":{"kind":"arxiv","id":"2307.08327","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.08327","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"arxiv_version","alias_value":"2307.08327v2","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08327","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_12","alias_value":"36LAZHUZH53R","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_16","alias_value":"36LAZHUZH53RMDAR","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_8","alias_value":"36LAZHUZ","created_at":"2026-07-05T12:09:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:36LAZHUZH53RMDARXYEKCSNDPV","target":"record","payload":{"canonical_record":{"source":{"id":"2307.08327","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-17T08:50:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9e098d1b29b152a30b5809b3e063870903fa3559ef993f691efb793ee60ffb3e","abstract_canon_sha256":"e32acd1f6d0697995cf76b80a398ddd358a553ecc82e57ae2570818555cb73d3"},"schema_version":"1.0"},"canonical_sha256":"df960c9e993f77160c11be08a149a37d7c58dcee53deca8ac39a225a040b87ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:41.380059Z","signature_b64":"DFrXJfHuypfLK0oSIOxG63xkwdDwrdx5JjfPDCF6d57c1RrcyV3rQXQJ/SO0D9PqxIb3Ps7GhFPGpjwfkAO9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df960c9e993f77160c11be08a149a37d7c58dcee53deca8ac39a225a040b87ff","last_reissued_at":"2026-07-05T12:09:41.379591Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:41.379591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.08327","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":"2026-07-05T12:09:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f4FJOUhi92VC1XXT8jZBtDkTRyszLsF/kEFJ95GjMNxZUv3lHj/7leYfM9fIEC7SbimSxc1Mn4D8natj5rFMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T22:36:52.701044Z"},"content_sha256":"993243816677e9235d29f506bf02b83d804c08173488208a2bac3720ae217fc7","schema_version":"1.0","event_id":"sha256:993243816677e9235d29f506bf02b83d804c08173488208a2bac3720ae217fc7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:36LAZHUZH53RMDARXYEKCSNDPV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Analyzing the Impact of Adversarial Examples on Explainable Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Prathyusha Devabhakthini, Raj Mani Shukla, Sasmita Parida, Suvendu Chandan Nayak, Tapadhir Das","submitted_at":"2023-07-17T08:50:36Z","abstract_excerpt":"Adversarial attacks are a type of attack on machine learning models where an attacker deliberately modifies the inputs to cause the model to make incorrect predictions. Adversarial attacks can have serious consequences, particularly in applications such as autonomous vehicles, medical diagnosis, and security systems. Work on the vulnerability of deep learning models to adversarial attacks has shown that it is very easy to make samples that make a model predict things that it doesn't want to. In this work, we analyze the impact of model interpretability due to adversarial attacks on text classi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08327","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/2307.08327/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":"2026-07-05T12:09:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EvTs8RJU3bO1gcpoZC7/xQri9ZBeNul1E6qeccNxWI4xyW95eFUJ+995D2ZkMj0cO3vj5IyTtTqHk63XADtTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T22:36:52.701600Z"},"content_sha256":"590d7b45c9b050bd8fc881ba0378380a6d07f22d8359679c70ca62bdcdd26862","schema_version":"1.0","event_id":"sha256:590d7b45c9b050bd8fc881ba0378380a6d07f22d8359679c70ca62bdcdd26862"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/36LAZHUZH53RMDARXYEKCSNDPV/bundle.json","state_url":"https://pith.science/pith/36LAZHUZH53RMDARXYEKCSNDPV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/36LAZHUZH53RMDARXYEKCSNDPV/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":"2026-08-21T22:36:52Z","links":{"resolver":"https://pith.science/pith/36LAZHUZH53RMDARXYEKCSNDPV","bundle":"https://pith.science/pith/36LAZHUZH53RMDARXYEKCSNDPV/bundle.json","state":"https://pith.science/pith/36LAZHUZH53RMDARXYEKCSNDPV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/36LAZHUZH53RMDARXYEKCSNDPV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:36LAZHUZH53RMDARXYEKCSNDPV","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":"e32acd1f6d0697995cf76b80a398ddd358a553ecc82e57ae2570818555cb73d3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-17T08:50:36Z","title_canon_sha256":"9e098d1b29b152a30b5809b3e063870903fa3559ef993f691efb793ee60ffb3e"},"schema_version":"1.0","source":{"id":"2307.08327","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.08327","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"arxiv_version","alias_value":"2307.08327v2","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08327","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_12","alias_value":"36LAZHUZH53R","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_16","alias_value":"36LAZHUZH53RMDAR","created_at":"2026-07-05T12:09:41Z"},{"alias_kind":"pith_short_8","alias_value":"36LAZHUZ","created_at":"2026-07-05T12:09:41Z"}],"graph_snapshots":[{"event_id":"sha256:590d7b45c9b050bd8fc881ba0378380a6d07f22d8359679c70ca62bdcdd26862","target":"graph","created_at":"2026-07-05T12:09:41Z","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/2307.08327/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adversarial attacks are a type of attack on machine learning models where an attacker deliberately modifies the inputs to cause the model to make incorrect predictions. Adversarial attacks can have serious consequences, particularly in applications such as autonomous vehicles, medical diagnosis, and security systems. Work on the vulnerability of deep learning models to adversarial attacks has shown that it is very easy to make samples that make a model predict things that it doesn't want to. In this work, we analyze the impact of model interpretability due to adversarial attacks on text classi","authors_text":"Prathyusha Devabhakthini, Raj Mani Shukla, Sasmita Parida, Suvendu Chandan Nayak, Tapadhir Das","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-17T08:50:36Z","title":"Analyzing the Impact of Adversarial Examples on Explainable Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08327","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":"sha256:993243816677e9235d29f506bf02b83d804c08173488208a2bac3720ae217fc7","target":"record","created_at":"2026-07-05T12:09:41Z","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":"e32acd1f6d0697995cf76b80a398ddd358a553ecc82e57ae2570818555cb73d3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-17T08:50:36Z","title_canon_sha256":"9e098d1b29b152a30b5809b3e063870903fa3559ef993f691efb793ee60ffb3e"},"schema_version":"1.0","source":{"id":"2307.08327","kind":"arxiv","version":2}},"canonical_sha256":"df960c9e993f77160c11be08a149a37d7c58dcee53deca8ac39a225a040b87ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df960c9e993f77160c11be08a149a37d7c58dcee53deca8ac39a225a040b87ff","first_computed_at":"2026-07-05T12:09:41.379591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:41.379591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DFrXJfHuypfLK0oSIOxG63xkwdDwrdx5JjfPDCF6d57c1RrcyV3rQXQJ/SO0D9PqxIb3Ps7GhFPGpjwfkAO9BA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:41.380059Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.08327","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:993243816677e9235d29f506bf02b83d804c08173488208a2bac3720ae217fc7","sha256:590d7b45c9b050bd8fc881ba0378380a6d07f22d8359679c70ca62bdcdd26862"],"state_sha256":"9094ece515f773764c4f7ad12a61e75be70264987e2a4faa12241c7514957052"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HADmvHg3+icLQd4DZ5MOPku0cfMnSY/SxD3+zONXhQ6m2/MPyshovs0oX4rENGT2hjfPAOqOwMGzRsKA45KjAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T22:36:52.706462Z","bundle_sha256":"d7f22fdec609669015578e4f79f33fbaea16d514d615887076dfc726f2e1a89d"}}