{"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/seeing-is-not-believing-camouflage-attacks-on","title":"Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms","arxiv_id":null,"date":"2019-08-01","proceeding":"USENIX Security Symposium 2019 8","authors":["Qixue Xiao","Yufei Chen","Chao Shen","Yu Chen","Kang Li"],"abstract":"Image scaling algorithms are intended to preserve the visual features before and after scaling, which is commonly used in numerous visual and image processing applications. In this paper, we demonstrate an automated attack against common scaling algorithms, i.e. to automatically generate camouflage images whose visual semantics change dramatically after scaling. To illustrate the threats from such camouflage attacks, we choose several computer vision applications as targeted victims, including multiple image classification applications based on popular deep learning frameworks, as well as main-stream web browsers. Our experimental results show that such attacks can cause different visual results after scaling and thus create evasion or data poisoning effect to these victim applications. We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud. To defend against such attacks, this paper suggests a few potential countermeasures from attack prevention to detection.","url_abs":"https://www.usenix.org/conference/usenixsecurity19/presentation/xiao","url_pdf":"https://www.usenix.org/system/files/sec19-xiao.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":"seeing-is-not-believing-camouflage-attacks-on","repo_url":"https://github.com/yfchen1994/scaling_camouflage","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-poisoning","task_name":"Data Poisoning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}