{"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-attacks-on-time-series","title":"Adversarial Attacks on Time Series","arxiv_id":"1902.10755","date":"2019-02-27","proceeding":null,"authors":["Fazle Karim","Somshubra Majumdar","Houshang Darabi"],"abstract":"Time series classification models have been garnering significant importance\nin the research community. However, not much research has been done on\ngenerating adversarial samples for these models. These adversarial samples can\nbecome a security concern. In this paper, we propose utilizing an adversarial\ntransformation network (ATN) on a distilled model to attack various time series\nclassification models. The proposed attack on the classification model utilizes\na distilled model as a surrogate that mimics the behavior of the attacked\nclassical time series classification models. Our proposed methodology is\napplied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN ) DTW, a Fully\nConnected Network and a Fully Convolutional Network (FCN), all of which are\ntrained on 42 University of California Riverside (UCR) datasets. In this paper,\nwe show both models were susceptible to attacks on all 42 datasets. To the best\nof our knowledge, such an attack on time series classification models has never\nbeen done before. Finally, we recommend future researchers that develop time\nseries classification models to incorporating adversarial data samples into\ntheir training data sets to improve resilience on adversarial samples and to\nconsider model robustness as an evaluative metric.","url_abs":"http://arxiv.org/abs/1902.10755v2","url_pdf":"http://arxiv.org/pdf/1902.10755v2.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-attacks-on-time-series","repo_url":"https://github.com/houshd/TS_Adv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"adversarial-attacks-on-time-series","repo_url":"https://github.com/titu1994/Adversarial-Attacks-Time-Series","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"dtw","method_name":"DTW"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.10755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}