{"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/mitigating-information-leakage-in-image","title":"Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach","arxiv_id":"1904.05514","date":"2019-04-11","proceeding":"CVPR 2019 6","authors":["Proteek Chandan Roy","Vishnu Naresh Boddeti"],"abstract":"Image recognition systems have demonstrated tremendous progress over the past\nfew decades thanks, in part, to our ability of learning compact and robust\nrepresentations of images. As we witness the wide spread adoption of these\nsystems, it is imperative to consider the problem of unintended leakage of\ninformation from an image representation, which might compromise the privacy of\nthe data owner. This paper investigates the problem of learning an image\nrepresentation that minimizes such leakage of user information. We formulate\nthe problem as an adversarial non-zero sum game of finding a good embedding\nfunction with two competing goals: to retain as much task dependent\ndiscriminative image information as possible, while simultaneously minimizing\nthe amount of information, as measured by entropy, about other sensitive\nattributes of the user. We analyze the stability and convergence dynamics of\nthe proposed formulation using tools from non-linear systems theory and compare\nto that of the corresponding adversarial zero-sum game formulation that\noptimizes likelihood as a measure of information content. Numerical experiments\non UCI, Extended Yale B, CIFAR-10 and CIFAR-100 datasets indicate that our\nproposed approach is able to learn image representations that exhibit high task\nperformance while mitigating leakage of predefined sensitive information.","url_abs":"http://arxiv.org/abs/1904.05514v1","url_pdf":"http://arxiv.org/pdf/1904.05514v1.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":"mitigating-information-leakage-in-image","repo_url":"https://github.com/human-analysis/MaxEnt-ARL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.05514","atlas_url":"https://app.syntology.ai/?focus=1904.05514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05514"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. 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