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Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression", "identifiers" : [ { "otype" : "PublicationIdentifier", "mtid" : 25924891, "link" : "/api/publicationidentifier/25924891", "label" : "DOI: 10.14722/ndss.2023.23165", "source" : { "otype" : "PlainSource", "mtid" : 6, "link" : "/api/publicationsource/6", "label" : "DOI", "type" : { "otype" : "PublicationSourceType", "mtid" : 10001, "link" : "/api/publicationsourcetype/10001", "label" : "DOI", "mayHaveOa" : true, "published" : true, "snippet" : true }, "name" : "DOI", "nameEng" : "DOI", "linkPattern" : "https://doi.org/@@@", "publiclyVisible" : true, "published" : true, "oldId" : 6, "snippet" : true }, "idValue" : "10.14722/ndss.2023.23165", "realUrl" : "https://doi.org/10.14722/ndss.2023.23165", "published" : false, "snippet" : true }, { "otype" : "PublicationIdentifier", "mtid" : 25924890, "link" : "/api/publicationidentifier/25924890", "label" : "Scopus: 85153453241", "source" : { 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In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems. Our attack framework, dubbed RoVISQ, manipulates the Rate-Distortion (R-D) relationship of a video compression model to achieve one or both of the following goals: (1) increasing the network bandwidth, (2) degrading the video quality for end-users. We further devise new objectives for targeted and untargeted attacks to a downstream video classification service. Finally, we design an input-invariant perturbation that universally disrupts video compression and classification systems in real time. Unlike previously proposed attacks on video classification, our adversarial perturbations are the first to withstand compression. We empirically show the resilience of RoVISQ attacks against various defenses, i.e., adversarial training, video denoising, and JPEG compression. Our extensive experimental results on various video datasets show RoVISQ attacks deteriorate peak signal-to-noise ratio by up to 5.6dB and the bit-rate by up to ∼ 2.4× while achieving over 90% attack success rate on a downstream classifier. Our user study further demonstrates the effect of RoVISQ attacks on users’ QoE. We provide several example attacked videos used in our survey on https://sites.google.com/view/demo-of-rovisq/home. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. 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