{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:P6GSNCHDDEHGABVRYIWS6EFHBH","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":"722313558bcb8d02d331fc74091ce8e5f8cdf298e562f513b5da5b62c81c9745","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-23T01:15:32Z","title_canon_sha256":"faa470bed2422499c29341567062d3ee32c18712d9dcff95b4b6a0d8c32429ac"},"schema_version":"1.0","source":{"id":"1911.10291","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.10291","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"arxiv_version","alias_value":"1911.10291v1","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.10291","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_12","alias_value":"P6GSNCHDDEHG","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6GSNCHDDEHGABVR","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6GSNCHD","created_at":"2026-07-05T00:22:43Z"}],"graph_snapshots":[{"event_id":"sha256:eb75ebffeff07f254a1fd9021f123723059d907b8e2773126fb37608ce68068c","target":"graph","created_at":"2026-07-05T00:22:43Z","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/1911.10291/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framework for solving the inference problem in GANs, which involves training an encoder network capable of inverting a pre-trained generator network without access to any training data. Under mild assumptions, we theoretically show that using InvGAN, we can approximately invert the generations of any latent code of a trained GAN model. Furthermore, we empirically demonstrate the superiority of our inference scheme by quantit","authors_text":"Pouya Samangouei, Rama Chellappa, Wei-An Lin, Yogesh Balaji","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-23T01:15:32Z","title":"Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.10291","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:bd2ef801735e02be21184a050b5971587e4a6831e30fa5c868ac28390978d172","target":"record","created_at":"2026-07-05T00:22:43Z","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":"722313558bcb8d02d331fc74091ce8e5f8cdf298e562f513b5da5b62c81c9745","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-23T01:15:32Z","title_canon_sha256":"faa470bed2422499c29341567062d3ee32c18712d9dcff95b4b6a0d8c32429ac"},"schema_version":"1.0","source":{"id":"1911.10291","kind":"arxiv","version":1}},"canonical_sha256":"7f8d2688e3190e6006b1c22d2f10a709efe4f1a99b151fc9daa2adcff55b8938","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f8d2688e3190e6006b1c22d2f10a709efe4f1a99b151fc9daa2adcff55b8938","first_computed_at":"2026-07-05T00:22:43.549676Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:22:43.549676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FVsgOg79GGc1UIjmAd48HyiqOUm4RSb7FcUzRV1iWmfGDVcNHgWFONxgEZVkY7/Pl+j8MVBgLIAPGa8E5hqWAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:22:43.550078Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.10291","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd2ef801735e02be21184a050b5971587e4a6831e30fa5c868ac28390978d172","sha256:eb75ebffeff07f254a1fd9021f123723059d907b8e2773126fb37608ce68068c"],"state_sha256":"eab037631e84b23c48067e471c940e613c72f2818bce4468d09fbcd7dca897fe"}