{"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/deep-spying-spying-using-smartwatch-and-deep","title":"Deep-Spying: Spying using Smartwatch and Deep Learning","arxiv_id":"1512.05616","date":"2015-12-17","proceeding":null,"authors":["Tony Beltramelli","Sebastian Risi"],"abstract":"Wearable technologies are today on the rise, becoming more common and broadly\navailable to mainstream users. In fact, wristband and armband devices such as\nsmartwatches and fitness trackers already took an important place in the\nconsumer electronics market and are becoming ubiquitous. By their very nature\nof being wearable, these devices, however, provide a new pervasive attack\nsurface threatening users privacy, among others.\n  In the meantime, advances in machine learning are providing unprecedented\npossibilities to process complex data efficiently. Allowing patterns to emerge\nfrom high dimensional unavoidably noisy data.\n  The goal of this work is to raise awareness about the potential risks related\nto motion sensors built-in wearable devices and to demonstrate abuse\nopportunities leveraged by advanced neural network architectures.\n  The LSTM-based implementation presented in this research can perform\ntouchlogging and keylogging on 12-keys keypads with above-average accuracy even\nwhen confronted with raw unprocessed data. Thus demonstrating that deep neural\nnetworks are capable of making keystroke inference attacks based on motion\nsensors easier to achieve by removing the need for non-trivial pre-processing\npipelines and carefully engineered feature extraction strategies. Our results\nsuggest that the complete technological ecosystem of a user can be compromised\nwhen a wearable wristband device is worn.","url_abs":"http://arxiv.org/abs/1512.05616v1","url_pdf":"http://arxiv.org/pdf/1512.05616v1.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":"deep-spying-spying-using-smartwatch-and-deep","repo_url":"https://github.com/tonybeltramelli/Deep-Spying","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep 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}