{"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/crafting-adversarial-input-sequences-for","title":"Crafting Adversarial Input Sequences for Recurrent Neural Networks","arxiv_id":"1604.08275","date":"2016-04-28","proceeding":null,"authors":["Nicolas Papernot","Patrick McDaniel","Ananthram Swami","Richard Harang"],"abstract":"Machine learning models are frequently used to solve complex security\nproblems, as well as to make decisions in sensitive situations like guiding\nautonomous vehicles or predicting financial market behaviors. Previous efforts\nhave shown that numerous machine learning models were vulnerable to adversarial\nmanipulations of their inputs taking the form of adversarial samples. Such\ninputs are crafted by adding carefully selected perturbations to legitimate\ninputs so as to force the machine learning model to misbehave, for instance by\noutputting a wrong class if the machine learning task of interest is\nclassification. In fact, to the best of our knowledge, all previous work on\nadversarial samples crafting for neural network considered models used to solve\nclassification tasks, most frequently in computer vision applications. In this\npaper, we contribute to the field of adversarial machine learning by\ninvestigating adversarial input sequences for recurrent neural networks\nprocessing sequential data. We show that the classes of algorithms introduced\npreviously to craft adversarial samples misclassified by feed-forward neural\nnetworks can be adapted to recurrent neural networks. In a experiment, we show\nthat adversaries can craft adversarial sequences misleading both categorical\nand sequential recurrent neural networks.","url_abs":"http://arxiv.org/abs/1604.08275v1","url_pdf":"http://arxiv.org/pdf/1604.08275v1.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":"crafting-adversarial-input-sequences-for","repo_url":"https://github.com/Bhushan-Jagtap-2013/Adversarial_Attack_on_RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.08275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.08275"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Bhushan-Jagtap-2013/Adversarial_Attack_on_RNN","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"ae7a589f65750d71","entry":"generate_data","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"ae7a589f65750d71"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}