{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:DUWQFPYEA6BM6Z3ON7TEJN3IQL","short_pith_number":"pith:DUWQFPYE","schema_version":"1.0","canonical_sha256":"1d2d02bf040782cf676e6fe644b76882d4d81788083dfc1bc182c70d97a56a81","source":{"kind":"arxiv","id":"1711.02879","version":1},"attestation_state":"computed","paper":{"title":"LatentPoison - Adversarial Attacks On The Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Anil A. Bharath, Antonia Creswell, Biswa Sengupta","submitted_at":"2017-11-08T09:37:16Z","abstract_excerpt":"Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms. Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep "},"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":"1711.02879","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-08T09:37:16Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"18293e7d8ab4a6720a4e7ee39f4ed01906a1ad54a5451835738082ce70b8f9ff","abstract_canon_sha256":"0ad2ca5868fd6d600b2cd49589fbd6288c84b7a00ae5c1249fdad533fd1d5895"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:01.478744Z","signature_b64":"p6SaX7JrESqONp1xCH9WktQv2ay+WTiH/a/DTxJpc6SJmyTC7+4I2J5YA4xbabbCFDNmeLOgLUJKkaJy+pEwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d2d02bf040782cf676e6fe644b76882d4d81788083dfc1bc182c70d97a56a81","last_reissued_at":"2026-05-18T00:31:01.478102Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:01.478102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LatentPoison - Adversarial Attacks On The Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Anil A. Bharath, Antonia Creswell, Biswa Sengupta","submitted_at":"2017-11-08T09:37:16Z","abstract_excerpt":"Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms. Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02879","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":""},"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":"1711.02879","created_at":"2026-05-18T00:31:01.478195+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.02879v1","created_at":"2026-05-18T00:31:01.478195+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02879","created_at":"2026-05-18T00:31:01.478195+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUWQFPYEA6BM","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUWQFPYEA6BM6Z3O","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUWQFPYE","created_at":"2026-05-18T12:31:12.930513+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.12009","citing_title":"LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL","json":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL.json","graph_json":"https://pith.science/api/pith-number/DUWQFPYEA6BM6Z3ON7TEJN3IQL/graph.json","events_json":"https://pith.science/api/pith-number/DUWQFPYEA6BM6Z3ON7TEJN3IQL/events.json","paper":"https://pith.science/paper/DUWQFPYE"},"agent_actions":{"view_html":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL","download_json":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL.json","view_paper":"https://pith.science/paper/DUWQFPYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.02879&json=true","fetch_graph":"https://pith.science/api/pith-number/DUWQFPYEA6BM6Z3ON7TEJN3IQL/graph.json","fetch_events":"https://pith.science/api/pith-number/DUWQFPYEA6BM6Z3ON7TEJN3IQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL/action/storage_attestation","attest_author":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL/action/author_attestation","sign_citation":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL/action/citation_signature","submit_replication":"https://pith.science/pith/DUWQFPYEA6BM6Z3ON7TEJN3IQL/action/replication_record"}},"created_at":"2026-05-18T00:31:01.478195+00:00","updated_at":"2026-05-18T00:31:01.478195+00:00"}