{"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/adversarial-playground-a-visualization-suite-1","title":"Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation","arxiv_id":"1706.01763","date":"2017-06-06","proceeding":null,"authors":["Andrew Norton","Yanjun Qi"],"abstract":"With growing interest in adversarial machine learning, it is important for\nmachine learning practitioners and users to understand how their models may be\nattacked. We propose a web-based visualization tool, Adversarial-Playground, to\ndemonstrate the efficacy of common adversarial methods against a deep neural\nnetwork (DNN) model, built on top of the TensorFlow library.\nAdversarial-Playground provides users an efficient and effective experience in\nexploring techniques generating adversarial examples, which are inputs crafted\nby an adversary to fool a machine learning system. To enable\nAdversarial-Playground to generate quick and accurate responses for users, we\nuse two primary tactics: (1) We propose a faster variant of the\nstate-of-the-art Jacobian saliency map approach that maintains a comparable\nevasion rate. (2) Our visualization does not transmit the generated adversarial\nimages to the client, but rather only the matrix describing the sample and the\nvector representing classification likelihoods.\n  The source code along with the data from all of our experiments are available\nat \\url{https://github.com/QData/AdversarialDNN-Playground}.","url_abs":"http://arxiv.org/abs/1706.01763v2","url_pdf":"http://arxiv.org/pdf/1706.01763v2.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":"adversarial-playground-a-visualization-suite-1","repo_url":"https://github.com/QData/AdversarialDNN-Playground","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"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}