{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7R5GGT2K2PJBRYY5NNYIJ6NH7M","short_pith_number":"pith:7R5GGT2K","canonical_record":{"source":{"id":"1811.09310","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-22T21:10:52Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"ce4c3dd62b9c0c8dc678323a21c49ca71bc2d6b6634e7282e436d0f118cdf571","abstract_canon_sha256":"809942f39d928281176a0f71e117e9c4ea429f1cdbc6725b6a5adb276727194d"},"schema_version":"1.0"},"canonical_sha256":"fc7a634f4ad3d218e31d6b7084f9a7fb320ba5b1a4a3f7836878fde64790d959","source":{"kind":"arxiv","id":"1811.09310","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09310","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09310v1","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09310","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"7R5GGT2K2PJB","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7R5GGT2K2PJBRYY5","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7R5GGT2K","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7R5GGT2K2PJBRYY5NNYIJ6NH7M","target":"record","payload":{"canonical_record":{"source":{"id":"1811.09310","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-22T21:10:52Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"ce4c3dd62b9c0c8dc678323a21c49ca71bc2d6b6634e7282e436d0f118cdf571","abstract_canon_sha256":"809942f39d928281176a0f71e117e9c4ea429f1cdbc6725b6a5adb276727194d"},"schema_version":"1.0"},"canonical_sha256":"fc7a634f4ad3d218e31d6b7084f9a7fb320ba5b1a4a3f7836878fde64790d959","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:03.713308Z","signature_b64":"efQA4f2KBqoJ0J4jJ8xdf9cQaqbYzDD31Nw/Ex70lyBYChwaPoY2McrURRlx8yeC0sNFdagnu7xtRSJrkz2lAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc7a634f4ad3d218e31d6b7084f9a7fb320ba5b1a4a3f7836878fde64790d959","last_reissued_at":"2026-05-18T00:00:03.712918Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:03.712918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.09310","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":"2026-05-18T00:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jf4iO2lBeRUWg8OfDK4Utl5kYJA46CY0OnnnRmWu201wAkOtdDx5Sj8L/Kh2bLz3TRjUdfjMz2kMQgjMkAVcBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:12:31.282325Z"},"content_sha256":"e12a6b6ac41162c6ca99f633dcd3fca653d26b250529c1b5138bac64c4713dad","schema_version":"1.0","event_id":"sha256:e12a6b6ac41162c6ca99f633dcd3fca653d26b250529c1b5138bac64c4713dad"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7R5GGT2K2PJBRYY5NNYIJ6NH7M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Adnan Siraj Rakin, Deliang Fan, Zhezhi He","submitted_at":"2018-11-22T21:10:52Z","abstract_excerpt":"Recent development in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an adversarial example is a carefully modified image that is visually imperceptible to the original image but can cause DNN model to misclassify it. Training the network with Gaussian noise is an effective technique to perform model regularization, thus improving model robustness against input variation. Inspired by this classical method, we explore to utilize the regularization characteristic of noise injection to improv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09310","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":""},"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":"2026-05-18T00:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YN5Gx7aeu4eVdPR2g5kGKimbw2LnpKyyqTm8NvBEouC3hyYXMP0U9hYmR33/Lfm6T0HOMuf0c9eyrKEiVifhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:12:31.282866Z"},"content_sha256":"eeac8eabcbfa785545064ccd6dcfa4e284c5a89362ee3be7353d9b544085b071","schema_version":"1.0","event_id":"sha256:eeac8eabcbfa785545064ccd6dcfa4e284c5a89362ee3be7353d9b544085b071"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/bundle.json","state_url":"https://pith.science/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/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":"2026-05-26T07:12:31Z","links":{"resolver":"https://pith.science/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M","bundle":"https://pith.science/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/bundle.json","state":"https://pith.science/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7R5GGT2K2PJBRYY5NNYIJ6NH7M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7R5GGT2K2PJBRYY5NNYIJ6NH7M","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":"809942f39d928281176a0f71e117e9c4ea429f1cdbc6725b6a5adb276727194d","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-22T21:10:52Z","title_canon_sha256":"ce4c3dd62b9c0c8dc678323a21c49ca71bc2d6b6634e7282e436d0f118cdf571"},"schema_version":"1.0","source":{"id":"1811.09310","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09310","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09310v1","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09310","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"7R5GGT2K2PJB","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7R5GGT2K2PJBRYY5","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7R5GGT2K","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:eeac8eabcbfa785545064ccd6dcfa4e284c5a89362ee3be7353d9b544085b071","target":"graph","created_at":"2026-05-18T00:00:03Z","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":"Recent development in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an adversarial example is a carefully modified image that is visually imperceptible to the original image but can cause DNN model to misclassify it. Training the network with Gaussian noise is an effective technique to perform model regularization, thus improving model robustness against input variation. Inspired by this classical method, we explore to utilize the regularization characteristic of noise injection to improv","authors_text":"Adnan Siraj Rakin, Deliang Fan, Zhezhi He","cross_cats":["cs.CR","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-22T21:10:52Z","title":"Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09310","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:e12a6b6ac41162c6ca99f633dcd3fca653d26b250529c1b5138bac64c4713dad","target":"record","created_at":"2026-05-18T00:00:03Z","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":"809942f39d928281176a0f71e117e9c4ea429f1cdbc6725b6a5adb276727194d","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-22T21:10:52Z","title_canon_sha256":"ce4c3dd62b9c0c8dc678323a21c49ca71bc2d6b6634e7282e436d0f118cdf571"},"schema_version":"1.0","source":{"id":"1811.09310","kind":"arxiv","version":1}},"canonical_sha256":"fc7a634f4ad3d218e31d6b7084f9a7fb320ba5b1a4a3f7836878fde64790d959","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc7a634f4ad3d218e31d6b7084f9a7fb320ba5b1a4a3f7836878fde64790d959","first_computed_at":"2026-05-18T00:00:03.712918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:03.712918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"efQA4f2KBqoJ0J4jJ8xdf9cQaqbYzDD31Nw/Ex70lyBYChwaPoY2McrURRlx8yeC0sNFdagnu7xtRSJrkz2lAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:03.713308Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.09310","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e12a6b6ac41162c6ca99f633dcd3fca653d26b250529c1b5138bac64c4713dad","sha256:eeac8eabcbfa785545064ccd6dcfa4e284c5a89362ee3be7353d9b544085b071"],"state_sha256":"29024348e172e7b193e3c237adfd4928ebd81bbdd88ec8daf08de71e926a1d93"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SJ2F+9xa8bZbumQdEJeoNQZy8Ek2OJI/vToFfXuS59QDmSrRBJwLACTaPue6UHpHECipUyN4G8mCHkY0QagfBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:12:31.285775Z","bundle_sha256":"689484de065cafbc408c48820b6e8b9f02fa26d61860d3b97abc60d37f873e81"}}