{"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/refacing-reconstructing-anonymized-facial","title":"Refacing: reconstructing anonymized facial features using GANs","arxiv_id":"1810.06455","date":"2018-10-15","proceeding":null,"authors":["David Abramian","Anders Eklund"],"abstract":"Anonymization of medical images is necessary for protecting the identity of\nthe test subjects, and is therefore an essential step in data sharing. However,\nrecent developments in deep learning may raise the bar on the amount of\ndistortion that needs to be applied to guarantee anonymity. To test such\npossibilities, we have applied the novel CycleGAN unsupervised image-to-image\ntranslation framework on sagittal slices of T1 MR images, in order to\nreconstruct facial features from anonymized data. We applied the CycleGAN\nframework on both face-blurred and face-removed images. Our results show that\nface blurring may not provide adequate protection against malicious attempts at\nidentifying the subjects, while face removal provides more robust\nanonymization, but is still partially reversible.","url_abs":"http://arxiv.org/abs/1810.06455v2","url_pdf":"http://arxiv.org/pdf/1810.06455v2.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":"refacing-reconstructing-anonymized-facial","repo_url":"https://github.com/DavidAbramian/refacing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"unsupervised-image-to-image-translation","task_name":"Unsupervised Image-To-Image Translation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.06455","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}