{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:L222ZZO6LW6KCZYUHC3PDKME3I","short_pith_number":"pith:L222ZZO6","canonical_record":{"source":{"id":"1905.05186","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T16:44:03Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"cbf47dec19ebff30c0befbda695bb975ab284e082e3e40f15b472bdbd25fbdc5","abstract_canon_sha256":"0742caa89ba2a321a77c483bb3b6c3db132aec667e8bf9d4ac82eb3c1c2cd7bc"},"schema_version":"1.0"},"canonical_sha256":"5eb5ace5de5dbca1671438b6f1a984da357f329aa664e1822f6b686aed7365df","source":{"kind":"arxiv","id":"1905.05186","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05186","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05186v2","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05186","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"pith_short_12","alias_value":"L222ZZO6LW6K","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"L222ZZO6LW6KCZYU","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"L222ZZO6","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:L222ZZO6LW6KCZYUHC3PDKME3I","target":"record","payload":{"canonical_record":{"source":{"id":"1905.05186","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T16:44:03Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"cbf47dec19ebff30c0befbda695bb975ab284e082e3e40f15b472bdbd25fbdc5","abstract_canon_sha256":"0742caa89ba2a321a77c483bb3b6c3db132aec667e8bf9d4ac82eb3c1c2cd7bc"},"schema_version":"1.0"},"canonical_sha256":"5eb5ace5de5dbca1671438b6f1a984da357f329aa664e1822f6b686aed7365df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:15.081381Z","signature_b64":"UKu0cG//BtVPdMeyKjD9eZrYBllT+lxoM0DdjW66s20O6BlUGi9q5L324CQWABjuxoiqzPgO7Ack0iG+kyE2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5eb5ace5de5dbca1671438b6f1a984da357f329aa664e1822f6b686aed7365df","last_reissued_at":"2026-05-17T23:42:15.080738Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:15.080738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.05186","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":"2026-05-17T23:42:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"goxLQ6CrqT7k1bn+yZBkiB7yPmlkCDNtoklfXVGJTxVNvzqY6p6rZEVJwymvpERjEeAZ65BjCuB4G+idV+AUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T02:01:50.976011Z"},"content_sha256":"9bbdcc4ac116acda45f488f471d57e7b850516ec5fcdad0f95efe2ef10700985","schema_version":"1.0","event_id":"sha256:9bbdcc4ac116acda45f488f471d57e7b850516ec5fcdad0f95efe2ef10700985"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:L222ZZO6LW6KCZYUHC3PDKME3I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhishek Sinha, Balaji Krishnamurthy, Harshitha Machiraju, Mayank Singh, Nupur Kumari, Vineeth N Balasubramanian","submitted_at":"2019-05-13T16:44:03Z","abstract_excerpt":"Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against these adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers. Our analysis reveals that contrary to the input layer which is robust to adversarial attack, the latent layer of these robust models are highly susceptible to adversarial "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05186","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":""},"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":"2026-05-17T23:42:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t6D17p3V+OpbGnw1R4l9Y1S0GZO3xPkFdomZE8P775ZoJ9RZseAcnIQC5NXK4ps0sCQOEMeMps8c8v/TwsS1DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T02:01:50.976668Z"},"content_sha256":"76d8141da2dc3b976130755c18cf0791c8c8b309765ae2b36a4a5c817a562310","schema_version":"1.0","event_id":"sha256:76d8141da2dc3b976130755c18cf0791c8c8b309765ae2b36a4a5c817a562310"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L222ZZO6LW6KCZYUHC3PDKME3I/bundle.json","state_url":"https://pith.science/pith/L222ZZO6LW6KCZYUHC3PDKME3I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L222ZZO6LW6KCZYUHC3PDKME3I/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":"2026-06-08T02:01:50Z","links":{"resolver":"https://pith.science/pith/L222ZZO6LW6KCZYUHC3PDKME3I","bundle":"https://pith.science/pith/L222ZZO6LW6KCZYUHC3PDKME3I/bundle.json","state":"https://pith.science/pith/L222ZZO6LW6KCZYUHC3PDKME3I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L222ZZO6LW6KCZYUHC3PDKME3I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:L222ZZO6LW6KCZYUHC3PDKME3I","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":"0742caa89ba2a321a77c483bb3b6c3db132aec667e8bf9d4ac82eb3c1c2cd7bc","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T16:44:03Z","title_canon_sha256":"cbf47dec19ebff30c0befbda695bb975ab284e082e3e40f15b472bdbd25fbdc5"},"schema_version":"1.0","source":{"id":"1905.05186","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05186","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05186v2","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05186","created_at":"2026-05-17T23:42:15Z"},{"alias_kind":"pith_short_12","alias_value":"L222ZZO6LW6K","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"L222ZZO6LW6KCZYU","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"L222ZZO6","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:76d8141da2dc3b976130755c18cf0791c8c8b309765ae2b36a4a5c817a562310","target":"graph","created_at":"2026-05-17T23:42:15Z","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"},"paper":{"abstract_excerpt":"Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against these adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers. Our analysis reveals that contrary to the input layer which is robust to adversarial attack, the latent layer of these robust models are highly susceptible to adversarial ","authors_text":"Abhishek Sinha, Balaji Krishnamurthy, Harshitha Machiraju, Mayank Singh, Nupur Kumari, Vineeth N Balasubramanian","cross_cats":["cs.CR","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T16:44:03Z","title":"Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05186","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":"sha256:9bbdcc4ac116acda45f488f471d57e7b850516ec5fcdad0f95efe2ef10700985","target":"record","created_at":"2026-05-17T23:42:15Z","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":"0742caa89ba2a321a77c483bb3b6c3db132aec667e8bf9d4ac82eb3c1c2cd7bc","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T16:44:03Z","title_canon_sha256":"cbf47dec19ebff30c0befbda695bb975ab284e082e3e40f15b472bdbd25fbdc5"},"schema_version":"1.0","source":{"id":"1905.05186","kind":"arxiv","version":2}},"canonical_sha256":"5eb5ace5de5dbca1671438b6f1a984da357f329aa664e1822f6b686aed7365df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5eb5ace5de5dbca1671438b6f1a984da357f329aa664e1822f6b686aed7365df","first_computed_at":"2026-05-17T23:42:15.080738Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:15.080738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UKu0cG//BtVPdMeyKjD9eZrYBllT+lxoM0DdjW66s20O6BlUGi9q5L324CQWABjuxoiqzPgO7Ack0iG+kyE2Dw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:15.081381Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.05186","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bbdcc4ac116acda45f488f471d57e7b850516ec5fcdad0f95efe2ef10700985","sha256:76d8141da2dc3b976130755c18cf0791c8c8b309765ae2b36a4a5c817a562310"],"state_sha256":"d7f42875be67772f22e9866444c4d06cd1a5b8a4af97550f120e1e72eae144eb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U1sxSnySl5/7mFFNzY9VTJ4R1dFqFzOhny6FMAZMuUNOiaA+Lrhe7UGvvBErWB1qQXMKfizUtAfpimCmGWntAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T02:01:50.980485Z","bundle_sha256":"471fc87ddf25133994bcc825f194aff66a0b41ad07bcd6ee8428b9b3f744e456"}}