{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:CLP6RIGN6DDHBI3TYUEUYLUBUC","short_pith_number":"pith:CLP6RIGN","canonical_record":{"source":{"id":"2106.07873","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-15T04:19:26Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3410c1894f8e86c0eaf9b10824766a052d3b9a21030bbe34eb1a1922074cc369","abstract_canon_sha256":"ec02f19f0230915fa9b72348a5b3b4ead62bc79035fa703ab8886080c4652794"},"schema_version":"1.0"},"canonical_sha256":"12dfe8a0cdf0c670a373c5094c2e81a084a728d0dc23b25b9fdf786a76d14ca1","source":{"kind":"arxiv","id":"2106.07873","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.07873","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"arxiv_version","alias_value":"2106.07873v3","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07873","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_12","alias_value":"CLP6RIGN6DDH","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_16","alias_value":"CLP6RIGN6DDHBI3T","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_8","alias_value":"CLP6RIGN","created_at":"2026-07-05T06:35:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:CLP6RIGN6DDHBI3TYUEUYLUBUC","target":"record","payload":{"canonical_record":{"source":{"id":"2106.07873","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-15T04:19:26Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3410c1894f8e86c0eaf9b10824766a052d3b9a21030bbe34eb1a1922074cc369","abstract_canon_sha256":"ec02f19f0230915fa9b72348a5b3b4ead62bc79035fa703ab8886080c4652794"},"schema_version":"1.0"},"canonical_sha256":"12dfe8a0cdf0c670a373c5094c2e81a084a728d0dc23b25b9fdf786a76d14ca1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:41.939288Z","signature_b64":"IACe6LHHtmxYUIoYbiJrd9w7JaMQFrSCsslBEfXv5Folo/QghIW5gC+kLMVpcAGAhp7F+OLA7eFzf6gmwjsEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12dfe8a0cdf0c670a373c5094c2e81a084a728d0dc23b25b9fdf786a76d14ca1","last_reissued_at":"2026-07-05T06:35:41.938775Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:41.938775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.07873","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:35:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F7PqpWHCQ3nF09CBTIOtlkVvlsrUqD3Mo9fvtzQmGdm0fwlSwuOQdmf48zaA5G1f1NoYOAfGJIu/xSRbdoDXDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:55:12.287711Z"},"content_sha256":"c49efd452597f85e88160b8acfafa220c89d6316cb337f97071971badb80b78d","schema_version":"1.0","event_id":"sha256:c49efd452597f85e88160b8acfafa220c89d6316cb337f97071971badb80b78d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:CLP6RIGN6DDHBI3TYUEUYLUBUC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Tal Hassner, Vishal Asnani, Xiaoming Liu, Xi Yin","submitted_at":"2021-06-15T04:19:26Z","abstract_excerpt":"State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. Identifying and understanding manipulated media are crucial to mitigate the social concerns on the potential misuse of GMs. We propose to perform reverse engineering of GMs to infer model hyperparameters from the images generated by these models. We define a novel problem, ``model parsing\", as estimating GM network architectures and training loss functions by examining their generated images -- a task seemingly impossible for human beings. To tackle"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07873","kind":"arxiv","version":3},"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/2106.07873/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":"2026-07-05T06:35:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A/ju+jcnEUpkAbISrfiPTECuBzYocbHGGiG/P5slASZsrhQeCTJEdWrwz7DVSq7mf7W25DNwiiQT9OQJ2juVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:55:12.288096Z"},"content_sha256":"a1c6d3cd6786acb8613fbf85afe34a9b8e0612acda0d0f5592537db3918862b8","schema_version":"1.0","event_id":"sha256:a1c6d3cd6786acb8613fbf85afe34a9b8e0612acda0d0f5592537db3918862b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/bundle.json","state_url":"https://pith.science/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/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":"2026-07-05T15:55:12Z","links":{"resolver":"https://pith.science/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC","bundle":"https://pith.science/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/bundle.json","state":"https://pith.science/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CLP6RIGN6DDHBI3TYUEUYLUBUC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:CLP6RIGN6DDHBI3TYUEUYLUBUC","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":"ec02f19f0230915fa9b72348a5b3b4ead62bc79035fa703ab8886080c4652794","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-15T04:19:26Z","title_canon_sha256":"3410c1894f8e86c0eaf9b10824766a052d3b9a21030bbe34eb1a1922074cc369"},"schema_version":"1.0","source":{"id":"2106.07873","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.07873","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"arxiv_version","alias_value":"2106.07873v3","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07873","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_12","alias_value":"CLP6RIGN6DDH","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_16","alias_value":"CLP6RIGN6DDHBI3T","created_at":"2026-07-05T06:35:41Z"},{"alias_kind":"pith_short_8","alias_value":"CLP6RIGN","created_at":"2026-07-05T06:35:41Z"}],"graph_snapshots":[{"event_id":"sha256:a1c6d3cd6786acb8613fbf85afe34a9b8e0612acda0d0f5592537db3918862b8","target":"graph","created_at":"2026-07-05T06:35:41Z","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/2106.07873/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. Identifying and understanding manipulated media are crucial to mitigate the social concerns on the potential misuse of GMs. We propose to perform reverse engineering of GMs to infer model hyperparameters from the images generated by these models. We define a novel problem, ``model parsing\", as estimating GM network architectures and training loss functions by examining their generated images -- a task seemingly impossible for human beings. To tackle","authors_text":"Tal Hassner, Vishal Asnani, Xiaoming Liu, Xi Yin","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-15T04:19:26Z","title":"Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07873","kind":"arxiv","version":3},"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:c49efd452597f85e88160b8acfafa220c89d6316cb337f97071971badb80b78d","target":"record","created_at":"2026-07-05T06:35:41Z","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":"ec02f19f0230915fa9b72348a5b3b4ead62bc79035fa703ab8886080c4652794","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-06-15T04:19:26Z","title_canon_sha256":"3410c1894f8e86c0eaf9b10824766a052d3b9a21030bbe34eb1a1922074cc369"},"schema_version":"1.0","source":{"id":"2106.07873","kind":"arxiv","version":3}},"canonical_sha256":"12dfe8a0cdf0c670a373c5094c2e81a084a728d0dc23b25b9fdf786a76d14ca1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12dfe8a0cdf0c670a373c5094c2e81a084a728d0dc23b25b9fdf786a76d14ca1","first_computed_at":"2026-07-05T06:35:41.938775Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:35:41.938775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IACe6LHHtmxYUIoYbiJrd9w7JaMQFrSCsslBEfXv5Folo/QghIW5gC+kLMVpcAGAhp7F+OLA7eFzf6gmwjsEBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:35:41.939288Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.07873","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c49efd452597f85e88160b8acfafa220c89d6316cb337f97071971badb80b78d","sha256:a1c6d3cd6786acb8613fbf85afe34a9b8e0612acda0d0f5592537db3918862b8"],"state_sha256":"ee02e21b5ce896cae2eceb5ee197cd7c0940f69a9d38556cf6362d312e6d25eb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4a1NCEGadY8sQ/0qBmUlTAa8Xt4FQlv3BvDunp61qaviard8WIT/8ZnLfa4CwbxzrgOmZj9w4ztW0Y1siSpeAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T15:55:12.290776Z","bundle_sha256":"486515491a5e8835322d357b4c782c80e402071f64361d9f8140b6b7bdafe1b1"}}