{"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/the-artificial-minds-eye-resisting","title":"The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection","arxiv_id":"1604.04428","date":"2016-04-15","proceeding":null,"authors":["Harm Berntsen","Wouter Kuijper","Tom Heskes"],"abstract":"We introduce a novel artificial neural network architecture that integrates\nrobustness to adversarial input in the network structure. The main idea of our\napproach is to force the network to make predictions on what the given instance\nof the class under consideration would look like and subsequently test those\npredictions. By forcing the network to redraw the relevant parts of the image\nand subsequently comparing this new image to the original, we are having the\nnetwork give a \"proof\" of the presence of the object.","url_abs":"http://arxiv.org/abs/1604.04428v2","url_pdf":"http://arxiv.org/pdf/1604.04428v2.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":"the-artificial-minds-eye-resisting","repo_url":"https://github.com/hberntsen/resisting-adversarials","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}