{"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/combatting-adversarial-attacks-through","title":"Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach","arxiv_id":"1812.03087","date":"2018-12-07","proceeding":null,"authors":["Rajeev Sahay","Rehana Mahfuz","Aly El Gamal"],"abstract":"Machine Learning models are vulnerable to adversarial attacks that rely on\nperturbing the input data. This work proposes a novel strategy using\nAutoencoder Deep Neural Networks to defend a machine learning model against two\ngradient-based attacks: The Fast Gradient Sign attack and Fast Gradient attack.\nFirst we use an autoencoder to denoise the test data, which is trained with\nboth clean and corrupted data. Then, we reduce the dimension of the denoised\ndata using the hidden layer representation of another autoencoder. We perform\nthis experiment for multiple values of the bound of adversarial perturbations,\nand consider different numbers of reduced dimensions. When the test data is\npreprocessed using this cascaded pipeline, the tested deep neural network\nclassifier yields a much higher accuracy, thus mitigating the effect of the\nadversarial perturbation.","url_abs":"http://arxiv.org/abs/1812.03087v1","url_pdf":"http://arxiv.org/pdf/1812.03087v1.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":"combatting-adversarial-attacks-through","repo_url":"https://github.com/rajeevsahay/ae-defenses","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}