{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:H6C7JLVM4VYKQDLOLTDZSMLMIN","short_pith_number":"pith:H6C7JLVM","canonical_record":{"source":{"id":"2310.08073","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:50:43Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5e545c16babf843661bff053ca5c4388d25ecc9dc7375c99c9b2d1fc1213650f","abstract_canon_sha256":"a12ebf0fb20cc62333a7ee384da530b5401eb4a263bcf0f8b698ab5b9f018a37"},"schema_version":"1.0"},"canonical_sha256":"3f85f4aeace570a80d6e5cc799316c437bd7a0a328c024c40c59ddca845455b9","source":{"kind":"arxiv","id":"2310.08073","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08073","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08073v1","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08073","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_12","alias_value":"H6C7JLVM4VYK","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_16","alias_value":"H6C7JLVM4VYKQDLO","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_8","alias_value":"H6C7JLVM","created_at":"2026-07-05T07:00:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:H6C7JLVM4VYKQDLOLTDZSMLMIN","target":"record","payload":{"canonical_record":{"source":{"id":"2310.08073","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:50:43Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5e545c16babf843661bff053ca5c4388d25ecc9dc7375c99c9b2d1fc1213650f","abstract_canon_sha256":"a12ebf0fb20cc62333a7ee384da530b5401eb4a263bcf0f8b698ab5b9f018a37"},"schema_version":"1.0"},"canonical_sha256":"3f85f4aeace570a80d6e5cc799316c437bd7a0a328c024c40c59ddca845455b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:00.568725Z","signature_b64":"Dy+uu5vJ3Gh8l9zSaWBl0NBe56Ktfzq/SV1F99JzSQRLheGVK0X5HqE6isS+zZsE2HBq36FGY3ujZCFBiOB5Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f85f4aeace570a80d6e5cc799316c437bd7a0a328c024c40c59ddca845455b9","last_reissued_at":"2026-07-05T07:00:00.568214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:00.568214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.08073","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":"2026-07-05T07:00:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y+QADL5NsKoW/P6zh4sSl/tgoYczL54d+T09P1MKDRmnQL7kVeySmEOi/ZtdalIo94x8j4755k8JbhEuxUGYAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:37:10.580193Z"},"content_sha256":"960a0c331d7c52d7f11129f1187f8295450892f1203c8a34cb021d628f32e186","schema_version":"1.0","event_id":"sha256:960a0c331d7c52d7f11129f1187f8295450892f1203c8a34cb021d628f32e186"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:H6C7JLVM4VYKQDLOLTDZSMLMIN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Ambra Demontis, Battista Biggio, Giorgio Piras, Maura Pintor","submitted_at":"2023-10-12T06:50:43Z","abstract_excerpt":"Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning methods can produce sparse networks while also preserving robustness to adversarial examples. In this work, we first re-evaluate three state-of-the-art adversarial pruning methods, showing that their robustness was indeed overestimated. We then compare pruned and dense versions of the same models, discovering that samples on thin ice, i.e., closer "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08073","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.08073/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:00:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oEr6AtCCBMhaVBZrEYnFISiGVS9EWOhVLhSdqI5pwUUSdOqCkOmYy+eijiHPXCuaqz+OlW5aoM2x5NxBhBnSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:37:10.580690Z"},"content_sha256":"c62e23588d76d6e447e8dbc947649ae9ca20ecf8d14b3e6dd2af4e0490a8da08","schema_version":"1.0","event_id":"sha256:c62e23588d76d6e447e8dbc947649ae9ca20ecf8d14b3e6dd2af4e0490a8da08"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/bundle.json","state_url":"https://pith.science/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/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":"2026-08-04T12:37:10Z","links":{"resolver":"https://pith.science/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN","bundle":"https://pith.science/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/bundle.json","state":"https://pith.science/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H6C7JLVM4VYKQDLOLTDZSMLMIN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:H6C7JLVM4VYKQDLOLTDZSMLMIN","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":"a12ebf0fb20cc62333a7ee384da530b5401eb4a263bcf0f8b698ab5b9f018a37","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:50:43Z","title_canon_sha256":"5e545c16babf843661bff053ca5c4388d25ecc9dc7375c99c9b2d1fc1213650f"},"schema_version":"1.0","source":{"id":"2310.08073","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08073","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08073v1","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08073","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_12","alias_value":"H6C7JLVM4VYK","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_16","alias_value":"H6C7JLVM4VYKQDLO","created_at":"2026-07-05T07:00:00Z"},{"alias_kind":"pith_short_8","alias_value":"H6C7JLVM","created_at":"2026-07-05T07:00:00Z"}],"graph_snapshots":[{"event_id":"sha256:c62e23588d76d6e447e8dbc947649ae9ca20ecf8d14b3e6dd2af4e0490a8da08","target":"graph","created_at":"2026-07-05T07:00:00Z","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/2310.08073/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning methods can produce sparse networks while also preserving robustness to adversarial examples. In this work, we first re-evaluate three state-of-the-art adversarial pruning methods, showing that their robustness was indeed overestimated. We then compare pruned and dense versions of the same models, discovering that samples on thin ice, i.e., closer ","authors_text":"Ambra Demontis, Battista Biggio, Giorgio Piras, Maura Pintor","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:50:43Z","title":"Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08073","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":"sha256:960a0c331d7c52d7f11129f1187f8295450892f1203c8a34cb021d628f32e186","target":"record","created_at":"2026-07-05T07:00:00Z","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":"a12ebf0fb20cc62333a7ee384da530b5401eb4a263bcf0f8b698ab5b9f018a37","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:50:43Z","title_canon_sha256":"5e545c16babf843661bff053ca5c4388d25ecc9dc7375c99c9b2d1fc1213650f"},"schema_version":"1.0","source":{"id":"2310.08073","kind":"arxiv","version":1}},"canonical_sha256":"3f85f4aeace570a80d6e5cc799316c437bd7a0a328c024c40c59ddca845455b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f85f4aeace570a80d6e5cc799316c437bd7a0a328c024c40c59ddca845455b9","first_computed_at":"2026-07-05T07:00:00.568214Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:00.568214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dy+uu5vJ3Gh8l9zSaWBl0NBe56Ktfzq/SV1F99JzSQRLheGVK0X5HqE6isS+zZsE2HBq36FGY3ujZCFBiOB5Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:00.568725Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08073","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:960a0c331d7c52d7f11129f1187f8295450892f1203c8a34cb021d628f32e186","sha256:c62e23588d76d6e447e8dbc947649ae9ca20ecf8d14b3e6dd2af4e0490a8da08"],"state_sha256":"fbfca8a5b1b430e844ebb13593b585b54294f3f54ed8854524ee988dc85ca96a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oRx/5Yjmu/smkOwRXMV4k946nG2nX+aL5qdl8ACmyg++t0NY6xKm9PA7POYT4lGsSfRd+vynCJksPEaaPsy0CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:37:10.585137Z","bundle_sha256":"2532cfe98926bd56c4c072d63598f2d04ca22e288ec20f1badaedbb05ed946b7"}}