{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/diagnosing-vulnerability-of-variational-auto","title":"Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks","arxiv_id":"2103.06701","date":"2021-03-10","proceeding":null,"authors":["Anna Kuzina","Max Welling","Jakub M. Tomczak"],"abstract":"In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just get a drastically different code (unsupervised attack). We examine the influence of model modifications ($\\beta$-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.","url_abs":"https://arxiv.org/abs/2103.06701v3","url_pdf":"https://arxiv.org/pdf/2103.06701v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"diagnosing-vulnerability-of-variational-auto","repo_url":"https://github.com/AKuzina/attack_vae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}