{"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/an-integrated-auto-encoder-block-switching-1","title":"An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks","arxiv_id":"2203.10930","date":"2022-03-11","proceeding":null,"authors":["Anirudh Yadav","Ashutosh Upadhyay","S. Sharanya"],"abstract":"According to recent studies, the vulnerability of state-of-the-art Neural Networks to adversarial input samples has increased drastically. A neural network is an intermediate path or technique by which a computer learns to perform tasks using Machine learning algorithms. Machine Learning and Artificial Intelligence model has become a fundamental aspect of life, such as self-driving cars [1], smart home devices, so any vulnerability is a significant concern. The smallest input deviations can fool these extremely literal systems and deceive their users as well as administrator into precarious situations. This article proposes a defense algorithm that utilizes the combination of an auto-encoder [3] and block-switching architecture. Auto-coder is intended to remove any perturbations found in input images whereas the block switching method is used to make it more robust against White-box attacks. The attack is planned using FGSM [9] model, and the subsequent counter-attack by the proposed architecture will take place thereby demonstrating the feasibility and security delivered by the algorithm.","url_abs":"https://arxiv.org/abs/2203.10930v1","url_pdf":"https://arxiv.org/pdf/2203.10930v1.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":"an-integrated-auto-encoder-block-switching-1","repo_url":"https://github.com/anirudh9784/Adversarial-Attacks-and-Defences","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"an-integrated-auto-encoder-block-switching-1","repo_url":"https://github.com/anirudh9784/Adversarial-Defense","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/adversarial-defense-on-miniimagenet","task":"Adversarial Defense","dataset":"miniImageNet","model":"Auto Encoder-Block Switching defense with GradCAM","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy ":"88.54"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}