{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LK2XEO443O32LZ24DYE6FTKKDK","short_pith_number":"pith:LK2XEO44","canonical_record":{"source":{"id":"1906.05599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T10:56:47Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"89a86ed53bd9dcb8fae1f73aa69a4c664971387ee353616d343759637f38b5f7","abstract_canon_sha256":"0a9d93dc5e100a7804be76770597a8242378f6867aa4e0a2b7cf30233e9e4b27"},"schema_version":"1.0"},"canonical_sha256":"5ab5723b9cdbb7a5e75c1e09e2cd4a1aa66b2ffce66f390532e539af7ef8c561","source":{"kind":"arxiv","id":"1906.05599","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.05599","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"arxiv_version","alias_value":"1906.05599v1","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05599","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"pith_short_12","alias_value":"LK2XEO443O32","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LK2XEO443O32LZ24","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LK2XEO44","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LK2XEO443O32LZ24DYE6FTKKDK","target":"record","payload":{"canonical_record":{"source":{"id":"1906.05599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T10:56:47Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"89a86ed53bd9dcb8fae1f73aa69a4c664971387ee353616d343759637f38b5f7","abstract_canon_sha256":"0a9d93dc5e100a7804be76770597a8242378f6867aa4e0a2b7cf30233e9e4b27"},"schema_version":"1.0"},"canonical_sha256":"5ab5723b9cdbb7a5e75c1e09e2cd4a1aa66b2ffce66f390532e539af7ef8c561","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:24.836063Z","signature_b64":"9WNiPT00MBuOl9Kwdyv3l5VBUS/FK77n+WrF2G/5bSHCfrXRdXUWRUPBNypt336bQSrmzUZLmWfOBp9wHS2EDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ab5723b9cdbb7a5e75c1e09e2cd4a1aa66b2ffce66f390532e539af7ef8c561","last_reissued_at":"2026-05-17T23:43:24.835425Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:24.835425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.05599","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":"2026-05-17T23:43:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V0kMXJbFeNZ0QXHTtwxWzaWJMc3wdDla4peblG3Uby9HxEIbmqahH8nFAoXcZK7YEUJrM5pO0ZstMAqPR7wODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T15:25:53.887819Z"},"content_sha256":"2ecc33c881d2e13d07b2b690d9b4e8325bc37cb434787a7699b04484ba9282b4","schema_version":"1.0","event_id":"sha256:2ecc33c881d2e13d07b2b690d9b4e8325bc37cb434787a7699b04484ba9282b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LK2XEO443O32LZ24DYE6FTKKDK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aly El Gamal, Rajeev Sahay, Rehana Mahfuz","submitted_at":"2019-06-13T10:56:47Z","abstract_excerpt":"The reliance on deep learning algorithms has grown significantly in recent years. Yet, these models are highly vulnerable to adversarial attacks, which introduce visually imperceptible perturbations into testing data to induce misclassifications. The literature has proposed several methods to combat such adversarial attacks, but each method either fails at high perturbation values, requires excessive computing power, or both. This letter proposes a computationally efficient method for defending the Fast Gradient Sign (FGS) adversarial attack by simultaneously denoising and compressing data. Sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05599","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":"2026-05-17T23:43:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"378nzjHo8sH8JLzk3y0aOVUh4XMXwW8da4pB+MCzQIGHgzFmH3XFk6mqOTwWCc2sgIlZ6meILuWWganFxgL1AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T15:25:53.888171Z"},"content_sha256":"d2725274c723a7ae4cd5aa18aa314f40bd5cc256eb5a26657dced024b80d549a","schema_version":"1.0","event_id":"sha256:d2725274c723a7ae4cd5aa18aa314f40bd5cc256eb5a26657dced024b80d549a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LK2XEO443O32LZ24DYE6FTKKDK/bundle.json","state_url":"https://pith.science/pith/LK2XEO443O32LZ24DYE6FTKKDK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LK2XEO443O32LZ24DYE6FTKKDK/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":"2026-06-05T15:25:53Z","links":{"resolver":"https://pith.science/pith/LK2XEO443O32LZ24DYE6FTKKDK","bundle":"https://pith.science/pith/LK2XEO443O32LZ24DYE6FTKKDK/bundle.json","state":"https://pith.science/pith/LK2XEO443O32LZ24DYE6FTKKDK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LK2XEO443O32LZ24DYE6FTKKDK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LK2XEO443O32LZ24DYE6FTKKDK","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":"0a9d93dc5e100a7804be76770597a8242378f6867aa4e0a2b7cf30233e9e4b27","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T10:56:47Z","title_canon_sha256":"89a86ed53bd9dcb8fae1f73aa69a4c664971387ee353616d343759637f38b5f7"},"schema_version":"1.0","source":{"id":"1906.05599","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.05599","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"arxiv_version","alias_value":"1906.05599v1","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05599","created_at":"2026-05-17T23:43:24Z"},{"alias_kind":"pith_short_12","alias_value":"LK2XEO443O32","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LK2XEO443O32LZ24","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LK2XEO44","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:d2725274c723a7ae4cd5aa18aa314f40bd5cc256eb5a26657dced024b80d549a","target":"graph","created_at":"2026-05-17T23:43:24Z","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":"The reliance on deep learning algorithms has grown significantly in recent years. Yet, these models are highly vulnerable to adversarial attacks, which introduce visually imperceptible perturbations into testing data to induce misclassifications. The literature has proposed several methods to combat such adversarial attacks, but each method either fails at high perturbation values, requires excessive computing power, or both. This letter proposes a computationally efficient method for defending the Fast Gradient Sign (FGS) adversarial attack by simultaneously denoising and compressing data. Sp","authors_text":"Aly El Gamal, Rajeev Sahay, Rehana Mahfuz","cross_cats":["cs.CR","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T10:56:47Z","title":"A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05599","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":"sha256:2ecc33c881d2e13d07b2b690d9b4e8325bc37cb434787a7699b04484ba9282b4","target":"record","created_at":"2026-05-17T23:43:24Z","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":"0a9d93dc5e100a7804be76770597a8242378f6867aa4e0a2b7cf30233e9e4b27","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T10:56:47Z","title_canon_sha256":"89a86ed53bd9dcb8fae1f73aa69a4c664971387ee353616d343759637f38b5f7"},"schema_version":"1.0","source":{"id":"1906.05599","kind":"arxiv","version":1}},"canonical_sha256":"5ab5723b9cdbb7a5e75c1e09e2cd4a1aa66b2ffce66f390532e539af7ef8c561","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ab5723b9cdbb7a5e75c1e09e2cd4a1aa66b2ffce66f390532e539af7ef8c561","first_computed_at":"2026-05-17T23:43:24.835425Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:24.835425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9WNiPT00MBuOl9Kwdyv3l5VBUS/FK77n+WrF2G/5bSHCfrXRdXUWRUPBNypt336bQSrmzUZLmWfOBp9wHS2EDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:24.836063Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.05599","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ecc33c881d2e13d07b2b690d9b4e8325bc37cb434787a7699b04484ba9282b4","sha256:d2725274c723a7ae4cd5aa18aa314f40bd5cc256eb5a26657dced024b80d549a"],"state_sha256":"1f8d9a3f602aad6c3020e0ceec4e18998c538184b730295e6f074c7b7de18802"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2tRpNgTbhvNeiuhnODKkvwe3/deWwwdEmbHiXL9JldYdFyJiZ1XrAgki9yvdvsPQbxbAx4oKB9N+rCUt49YRBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T15:25:53.893581Z","bundle_sha256":"88e6d5798cf6cae01d689c62f35522f9d2b49a17c9f973dab84560b28950f8b8"}}