{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QGOPMUUOIWRYSDETWH2RCHRDIN","short_pith_number":"pith:QGOPMUUO","schema_version":"1.0","canonical_sha256":"819cf6528e45a3890c93b1f5111e23434808be0d726c37f98fd7f07378e24c42","source":{"kind":"arxiv","id":"2201.04736","version":2},"attestation_state":"computed","paper":{"title":"Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Huaming Chen, M. Ali Babar","submitted_at":"2022-01-12T23:20:25Z","abstract_excerpt":"The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rush, and the latent vulnerabilities and privacy issues exposed to external"},"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":"2201.04736","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-01-12T23:20:25Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"b1ca41b118ab5328299489214885bcc4309bc7c205622210d883d4fa67948c71","abstract_canon_sha256":"c405001b8ad29c2cb5770c3ee1fb886943004d30b347a87bc3c566e61e38c20a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:45.138184Z","signature_b64":"S759cRf0prsiNoZA0Grzy0+VHutC0Ar5GoyVw4SQXmsRpbmJ4jN1wEWSzkehMK9G27+gUkW4JXmGnuE5bMhRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"819cf6528e45a3890c93b1f5111e23434808be0d726c37f98fd7f07378e24c42","last_reissued_at":"2026-07-05T07:24:45.137730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:45.137730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Huaming Chen, M. Ali Babar","submitted_at":"2022-01-12T23:20:25Z","abstract_excerpt":"The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rush, and the latent vulnerabilities and privacy issues exposed to external"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.04736","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/2201.04736/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":"2201.04736","created_at":"2026-07-05T07:24:45.137785+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.04736v2","created_at":"2026-07-05T07:24:45.137785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.04736","created_at":"2026-07-05T07:24:45.137785+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGOPMUUOIWRY","created_at":"2026-07-05T07:24:45.137785+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGOPMUUOIWRYSDET","created_at":"2026-07-05T07:24:45.137785+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGOPMUUO","created_at":"2026-07-05T07:24:45.137785+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN","json":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN.json","graph_json":"https://pith.science/api/pith-number/QGOPMUUOIWRYSDETWH2RCHRDIN/graph.json","events_json":"https://pith.science/api/pith-number/QGOPMUUOIWRYSDETWH2RCHRDIN/events.json","paper":"https://pith.science/paper/QGOPMUUO"},"agent_actions":{"view_html":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN","download_json":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN.json","view_paper":"https://pith.science/paper/QGOPMUUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.04736&json=true","fetch_graph":"https://pith.science/api/pith-number/QGOPMUUOIWRYSDETWH2RCHRDIN/graph.json","fetch_events":"https://pith.science/api/pith-number/QGOPMUUOIWRYSDETWH2RCHRDIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN/action/storage_attestation","attest_author":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN/action/author_attestation","sign_citation":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN/action/citation_signature","submit_replication":"https://pith.science/pith/QGOPMUUOIWRYSDETWH2RCHRDIN/action/replication_record"}},"created_at":"2026-07-05T07:24:45.137785+00:00","updated_at":"2026-07-05T07:24:45.137785+00:00"}