{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:27:06Z","timestamp":1760059626807,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Development of the Russian Federation","award":["25-139-66879-1-0003"],"award-info":[{"award-number":["25-139-66879-1-0003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Neural-network-based models have made considerable progress in many computer vision areas over recent years. However, many works have exposed their vulnerability to malicious input data manipulation\u2014that is, to adversarial attacks. Although many recent works have thoroughly examined the adversarial robustness of classifiers, the robustness of Image Quality Assessment (IQA) methods remains understudied. This paper addresses this gap by proposing FM-GOAT (Frequency-Masked Gradient Orthogonalization Attack), a novel white box adversarial method tailored for no-reference IQA models. Using a novel gradient orthogonalization technique, FM-GOAT uniquely optimizes adversarial perturbations against multiple perceptual constraints to minimize visibility, moving beyond traditional lp-norm bounds. We evaluate FM-GOAT on seven state-of-the-art NR-IQA models across three image and video datasets, revealing significant vulnerability to the proposed attack. Furthermore, we examine the applicability of adversarial purification methods to the IQA task, as well as their efficiency in mitigating white box adversarial attacks. By studying the activations from models\u2019 intermediate layers, we explore their behavioral patterns in adversarial scenarios and discover valuable insights that may lead to better adversarial detection.<\/jats:p>","DOI":"10.3390\/bdcc9070166","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T03:39:59Z","timestamp":1750909199000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7131-5839","authenticated-orcid":false,"given":"Khaled","family":"Abud","sequence":"first","affiliation":[{"name":"MSU Institute for Artificial Intelligence, Lomonosovsky Prospekt, 27, Building 1, 119192 Moscow, Russia"},{"name":"Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, GSP-1, 1-52, Leninskiye Gory, 119234 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8544-2923","authenticated-orcid":false,"given":"Sergey","family":"Lavrushkin","sequence":"additional","affiliation":[{"name":"MSU Institute for Artificial Intelligence, Lomonosovsky Prospekt, 27, Building 1, 119192 Moscow, Russia"},{"name":"ISP RAS Research Center for Trusted Artificial Intelligence, Alexander Solzhenitsyn St., 25, 109004 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-9340","authenticated-orcid":false,"given":"Dmitry","family":"Vatolin","sequence":"additional","affiliation":[{"name":"MSU Institute for Artificial Intelligence, Lomonosovsky Prospekt, 27, Building 1, 119192 Moscow, Russia"},{"name":"Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, GSP-1, 1-52, Leninskiye Gory, 119234 Moscow, Russia"},{"name":"ISP RAS Research Center for Trusted Artificial Intelligence, Alexander Solzhenitsyn St., 25, 109004 Moscow, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. 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