{"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/pseudo-random-number-generation-using","title":"Pseudo-Random Number Generation using Generative Adversarial Networks","arxiv_id":"1810.00378","date":"2018-09-30","proceeding":null,"authors":["Marcello De Bernardi","MHR Khouzani","Pasquale Malacaria"],"abstract":"Pseudo-random number generators (PRNG) are a fundamental element of many\nsecurity algorithms. We introduce a novel approach to their implementation, by\nproposing the use of generative adversarial networks (GAN) to train a neural\nnetwork to behave as a PRNG. Furthermore, we showcase a number of interesting\nmodifications to the standard GAN architecture. The most significant is\npartially concealing the output of the GAN's generator, and training the\nadversary to discover a mapping from the overt part to the concealed part. The\ngenerator therefore learns to produce values the adversary cannot predict,\nrather than to approximate an explicit reference distribution. We demonstrate\nthat a GAN can effectively train even a small feed-forward fully connected\nneural network to produce pseudo-random number sequences with good statistical\nproperties. At best, subjected to the NIST test suite, the trained generator\npassed around 99% of test instances and 98% of overall tests, outperforming a\nnumber of standard non-cryptographic PRNGs.","url_abs":"http://arxiv.org/abs/1810.00378v1","url_pdf":"http://arxiv.org/pdf/1810.00378v1.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":"pseudo-random-number-generation-using","repo_url":"https://github.com/marcellodebernardi/adversarial-csprng","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}