{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TTZOEKNXNM5OGVABQGBMOSL6WS","short_pith_number":"pith:TTZOEKNX","schema_version":"1.0","canonical_sha256":"9cf2e229b76b3ae354018182c7497eb4926aa2417cda9a8c8430d6fc5ceb3631","source":{"kind":"arxiv","id":"1702.04267","version":2},"attestation_state":"computed","paper":{"title":"On Detecting Adversarial Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Bastian Bischoff, Jan Hendrik Metzen, Tim Genewein, Volker Fischer","submitted_at":"2017-02-14T15:44:26Z","abstract_excerpt":"Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small \"detector\" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has"},"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":"1702.04267","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T15:44:26Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"e71f6ece0b2e1391039456ac2b045358574b1abbd697453b23c5e7cf64d5abd1","abstract_canon_sha256":"847a2686a899c0611244163069c90520ad069cc0e9d81b2b92b3642846747ea6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:16.706468Z","signature_b64":"JZX1VGIu10A4KbQjwoqV0NkUl0zLDd82Osmbf+g+SCzgjD8y0u02d21PKdV/R3sBFO5YLDRn8W1mbd65iG3zAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cf2e229b76b3ae354018182c7497eb4926aa2417cda9a8c8430d6fc5ceb3631","last_reissued_at":"2026-05-18T00:50:16.705831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:16.705831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Detecting Adversarial Perturbations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Bastian Bischoff, Jan Hendrik Metzen, Tim Genewein, Volker Fischer","submitted_at":"2017-02-14T15:44:26Z","abstract_excerpt":"Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small \"detector\" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04267","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1702.04267","created_at":"2026-05-18T00:50:16.705927+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.04267v2","created_at":"2026-05-18T00:50:16.705927+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04267","created_at":"2026-05-18T00:50:16.705927+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTZOEKNXNM5O","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTZOEKNXNM5OGVAB","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTZOEKNX","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2406.09250","citing_title":"MirrorCheck: Efficient Adversarial Defense for Vision-Language Models","ref_index":64,"is_internal_anchor":true},{"citing_arxiv_id":"2412.14738","citing_title":"Spectrally unstable nodes drive reliability failures in graph learning","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2510.17381","citing_title":"Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2603.13970","citing_title":"Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2309.00614","citing_title":"Baseline Defenses for Adversarial Attacks Against Aligned Language Models","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02780","citing_title":"A Unified Perspective on Adversarial Membership Manipulation in Vision Models","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27487","citing_title":"Low Rank Adaptation for Adversarial Perturbation","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01221","citing_title":"Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation","ref_index":296,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23593","citing_title":"When AI reviews science: Can we trust the referee?","ref_index":87,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS","json":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS.json","graph_json":"https://pith.science/api/pith-number/TTZOEKNXNM5OGVABQGBMOSL6WS/graph.json","events_json":"https://pith.science/api/pith-number/TTZOEKNXNM5OGVABQGBMOSL6WS/events.json","paper":"https://pith.science/paper/TTZOEKNX"},"agent_actions":{"view_html":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS","download_json":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS.json","view_paper":"https://pith.science/paper/TTZOEKNX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.04267&json=true","fetch_graph":"https://pith.science/api/pith-number/TTZOEKNXNM5OGVABQGBMOSL6WS/graph.json","fetch_events":"https://pith.science/api/pith-number/TTZOEKNXNM5OGVABQGBMOSL6WS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS/action/storage_attestation","attest_author":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS/action/author_attestation","sign_citation":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS/action/citation_signature","submit_replication":"https://pith.science/pith/TTZOEKNXNM5OGVABQGBMOSL6WS/action/replication_record"}},"created_at":"2026-05-18T00:50:16.705927+00:00","updated_at":"2026-05-18T00:50:16.705927+00:00"}