{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SWE43RJT732P5UKWJKUI5M672O","short_pith_number":"pith:SWE43RJT","canonical_record":{"source":{"id":"1801.02257","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-07T22:03:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"43bef95c0a673fe5635b34e46187353d129004af1f080ae2537c86e015058fb8","abstract_canon_sha256":"751cb03fd29d3a946e9db3ee8c3e21ce1ec8ce471e58f20d5a45baf122695a17"},"schema_version":"1.0"},"canonical_sha256":"9589cdc533fef4fed1564aa88eb3dfd3aa6157fc47901a43f3e96d38543819d9","source":{"kind":"arxiv","id":"1801.02257","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.02257","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"arxiv_version","alias_value":"1801.02257v1","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.02257","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"pith_short_12","alias_value":"SWE43RJT732P","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SWE43RJT732P5UKW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SWE43RJT","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SWE43RJT732P5UKWJKUI5M672O","target":"record","payload":{"canonical_record":{"source":{"id":"1801.02257","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-07T22:03:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"43bef95c0a673fe5635b34e46187353d129004af1f080ae2537c86e015058fb8","abstract_canon_sha256":"751cb03fd29d3a946e9db3ee8c3e21ce1ec8ce471e58f20d5a45baf122695a17"},"schema_version":"1.0"},"canonical_sha256":"9589cdc533fef4fed1564aa88eb3dfd3aa6157fc47901a43f3e96d38543819d9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:32.288036Z","signature_b64":"YE/i1bCXNaTI5UirC5H8SnkNCYg1+5fVeWYg2D0ZXY0upD9ZbzZrbP7AYnbATBLolE2umrX8M/VuTUH0ksQwCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9589cdc533fef4fed1564aa88eb3dfd3aa6157fc47901a43f3e96d38543819d9","last_reissued_at":"2026-05-18T00:26:32.287407Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:32.287407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.02257","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":"2026-05-18T00:26:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gDLe+43XsUkzucSOTexha1KiAeDczjln/Lft0UML4W89fhYUN+jDkGtAnjaPjkOJrrUKoF+8amLv5gUF9arCDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T16:58:20.789798Z"},"content_sha256":"07e28c656f893e04bf70e56859c1b12f6256c6d396c8bfca247e5e5e198092bf","schema_version":"1.0","event_id":"sha256:07e28c656f893e04bf70e56859c1b12f6256c6d396c8bfca247e5e5e198092bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SWE43RJT732P5UKWJKUI5M672O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Denoising Dictionary Learning Against Adversarial Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Derek Bridge, John Mitro, Steven Prestwich","submitted_at":"2018-01-07T22:03:20Z","abstract_excerpt":"We propose denoising dictionary learning (DDL), a simple yet effective technique as a protection measure against adversarial perturbations. We examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA). We evaluated it against five different deep neural networks (DNN) representing the building blocks of most recent architectures indicating a successive progression of model complexity of each other. We show that each model tends to capture different representations based on their "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.02257","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":"2026-05-18T00:26:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I5pLPJOKZquntPfVfCrXYy8ahRTri8lfUX07NRW9rmYBqBoJ9ajP3CrBYjhBKlWJvM2vCJQ7hkGJ6D7/JmwOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T16:58:20.790131Z"},"content_sha256":"a7b0e58d98e05bbde222ea0db2084cd61882b9f9bb6766183931b2eabdb3bf9e","schema_version":"1.0","event_id":"sha256:a7b0e58d98e05bbde222ea0db2084cd61882b9f9bb6766183931b2eabdb3bf9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SWE43RJT732P5UKWJKUI5M672O/bundle.json","state_url":"https://pith.science/pith/SWE43RJT732P5UKWJKUI5M672O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SWE43RJT732P5UKWJKUI5M672O/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":"2026-06-23T16:58:20Z","links":{"resolver":"https://pith.science/pith/SWE43RJT732P5UKWJKUI5M672O","bundle":"https://pith.science/pith/SWE43RJT732P5UKWJKUI5M672O/bundle.json","state":"https://pith.science/pith/SWE43RJT732P5UKWJKUI5M672O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SWE43RJT732P5UKWJKUI5M672O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SWE43RJT732P5UKWJKUI5M672O","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":"751cb03fd29d3a946e9db3ee8c3e21ce1ec8ce471e58f20d5a45baf122695a17","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-07T22:03:20Z","title_canon_sha256":"43bef95c0a673fe5635b34e46187353d129004af1f080ae2537c86e015058fb8"},"schema_version":"1.0","source":{"id":"1801.02257","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.02257","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"arxiv_version","alias_value":"1801.02257v1","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.02257","created_at":"2026-05-18T00:26:32Z"},{"alias_kind":"pith_short_12","alias_value":"SWE43RJT732P","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SWE43RJT732P5UKW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SWE43RJT","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:a7b0e58d98e05bbde222ea0db2084cd61882b9f9bb6766183931b2eabdb3bf9e","target":"graph","created_at":"2026-05-18T00:26:32Z","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":"We propose denoising dictionary learning (DDL), a simple yet effective technique as a protection measure against adversarial perturbations. We examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA). We evaluated it against five different deep neural networks (DNN) representing the building blocks of most recent architectures indicating a successive progression of model complexity of each other. We show that each model tends to capture different representations based on their ","authors_text":"Derek Bridge, John Mitro, Steven Prestwich","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-07T22:03:20Z","title":"Denoising Dictionary Learning Against Adversarial Perturbations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.02257","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":"sha256:07e28c656f893e04bf70e56859c1b12f6256c6d396c8bfca247e5e5e198092bf","target":"record","created_at":"2026-05-18T00:26:32Z","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":"751cb03fd29d3a946e9db3ee8c3e21ce1ec8ce471e58f20d5a45baf122695a17","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-07T22:03:20Z","title_canon_sha256":"43bef95c0a673fe5635b34e46187353d129004af1f080ae2537c86e015058fb8"},"schema_version":"1.0","source":{"id":"1801.02257","kind":"arxiv","version":1}},"canonical_sha256":"9589cdc533fef4fed1564aa88eb3dfd3aa6157fc47901a43f3e96d38543819d9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9589cdc533fef4fed1564aa88eb3dfd3aa6157fc47901a43f3e96d38543819d9","first_computed_at":"2026-05-18T00:26:32.287407Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:32.287407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YE/i1bCXNaTI5UirC5H8SnkNCYg1+5fVeWYg2D0ZXY0upD9ZbzZrbP7AYnbATBLolE2umrX8M/VuTUH0ksQwCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:32.288036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.02257","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07e28c656f893e04bf70e56859c1b12f6256c6d396c8bfca247e5e5e198092bf","sha256:a7b0e58d98e05bbde222ea0db2084cd61882b9f9bb6766183931b2eabdb3bf9e"],"state_sha256":"bfef9242cf2152ee8f309ac9ad7e3d5b428803c35455ef3aecbf2e19e6eacc00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qUj6aFp3Tdk9oeTZ3SZaPfDSAFx9z4WkGH+XWt3XtjhSyfTE5hdzlMxHDGVYGMVBJfR9zMP67eqUTyOn5W37Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T16:58:20.791993Z","bundle_sha256":"a5c4730edb2088b47da8935cd7ade183ab68cf324a3199f57363246e100e654b"}}