{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:OEXBZXSPMH5RLIJ5GERP6GWW2P","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":"909ac6775b18db6c5e18deb3d76611213fc022056882f21eac2765b6b3f5b2b1","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T21:06:11Z","title_canon_sha256":"1eee67b052fe48041b4fd647b87503c0f95da806696f86fb1c3e44f077aaffa2"},"schema_version":"1.0","source":{"id":"2211.00748","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00748","created_at":"2026-07-05T05:12:34Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00748v1","created_at":"2026-07-05T05:12:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00748","created_at":"2026-07-05T05:12:34Z"},{"alias_kind":"pith_short_12","alias_value":"OEXBZXSPMH5R","created_at":"2026-07-05T05:12:34Z"},{"alias_kind":"pith_short_16","alias_value":"OEXBZXSPMH5RLIJ5","created_at":"2026-07-05T05:12:34Z"},{"alias_kind":"pith_short_8","alias_value":"OEXBZXSP","created_at":"2026-07-05T05:12:34Z"}],"graph_snapshots":[{"event_id":"sha256:bbacb2fa98a6a6f8d52aae0e46f53a4b192197a3df7f0fa0d95ab3986ae88c30","target":"graph","created_at":"2026-07-05T05:12:34Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.00748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular. However, they are very susceptible to small adversarial perturbations that are carefully crafted to change the network decision. In this work, we build on knowledge distillation ideas and adversarial training in order to build more robust AMC systems. We first outline the importance of the quality of the training data in terms of accuracy and robustness of the model. We then propose to use the Maximum Likelihood function, which could solve the AMC problem in off","authors_text":"G\\'er\\^ome Bovet, Javier Maroto, Pascal Frossard","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T21:06:11Z","title":"Maximum Likelihood Distillation for Robust Modulation Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00748","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":"sha256:9b17b94cd403143acd2b95cf85a1662d3ff1d04c0f33dc909b3acff6bdeb0863","target":"record","created_at":"2026-07-05T05:12:34Z","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":"909ac6775b18db6c5e18deb3d76611213fc022056882f21eac2765b6b3f5b2b1","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T21:06:11Z","title_canon_sha256":"1eee67b052fe48041b4fd647b87503c0f95da806696f86fb1c3e44f077aaffa2"},"schema_version":"1.0","source":{"id":"2211.00748","kind":"arxiv","version":1}},"canonical_sha256":"712e1cde4f61fb15a13d3122ff1ad6d3ce6bf0eb394fa2e9fa326b30536b79c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"712e1cde4f61fb15a13d3122ff1ad6d3ce6bf0eb394fa2e9fa326b30536b79c3","first_computed_at":"2026-07-05T05:12:34.350180Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:34.350180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s3mC2pF9HfxvBKZm9OXrqVWqDs/HrOmQSnFRB9h5JT/DKnVK2uesokaGDpfSYSKdXW4A55kU5MwwBuPAXstbBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:34.351010Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.00748","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b17b94cd403143acd2b95cf85a1662d3ff1d04c0f33dc909b3acff6bdeb0863","sha256:bbacb2fa98a6a6f8d52aae0e46f53a4b192197a3df7f0fa0d95ab3986ae88c30"],"state_sha256":"0ca24e09b8f85154c5df8a95500bb1b44fcee9acdcd4afc37ba8e7057a69d2e2"}