{"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/whos-afraid-of-adversarial-queries-the-impact","title":"Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval","arxiv_id":"1901.10332","date":"2019-01-29","proceeding":null,"authors":["Zhuoran Liu","Zhengyu Zhao","Martha Larson"],"abstract":"An adversarial query is an image that has been modified to disrupt\ncontent-based image retrieval (CBIR) while appearing nearly untouched to the\nhuman eye. This paper presents an analysis of adversarial queries for CBIR\nbased on neural, local, and global features. We introduce an innovative neural\nimage perturbation approach, called Perturbations for Image Retrieval Error\n(PIRE), that is capable of blocking neural-feature-based CBIR. PIRE differs\nsignificantly from existing approaches that create images adversarial with\nrespect to CNN classifiers because it is unsupervised, i.e., it needs no\nlabelled data from the data set to which it is applied. Our experimental\nanalysis demonstrates the surprising effectiveness of PIRE in blocking CBIR,\nand also covers aspects of PIRE that must be taken into account in practical\nsettings, including saving images, image quality and leaking adversarial\nqueries into the background collection. Our experiments also compare PIRE (a\nneural approach) with existing keypoint removal and injection approaches (which\nmodify local features). Finally, we discuss the challenges that face multimedia\nresearchers in the future study of adversarial queries.","url_abs":"http://arxiv.org/abs/1901.10332v3","url_pdf":"http://arxiv.org/pdf/1901.10332v3.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":"whos-afraid-of-adversarial-queries-the-impact","repo_url":"https://github.com/liuzrcc/PIRE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"content-based-image-retrieval","task_name":"Content-Based Image Retrieval"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.10332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10332"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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