Gianluca Anselmi, Yash Vekaria, Alexander D'Souza, Patricia Callejo, Anna Maria Mandalari, Zubair Shafiq
{"title":"Watching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs","authors":"Gianluca Anselmi, Yash Vekaria, Alexander D'Souza, Patricia Callejo, Anna Maria Mandalari, Zubair Shafiq","doi":"arxiv-2409.06203","DOIUrl":null,"url":null,"abstract":"Smart TVs implement a unique tracking approach called Automatic Content\nRecognition (ACR) to profile viewing activity of their users. ACR is a\nShazam-like technology that works by periodically capturing the content\ndisplayed on a TV's screen and matching it against a content library to detect\nwhat content is being displayed at any given point in time. While prior\nresearch has investigated third-party tracking in the smart TV ecosystem, it\nhas not looked into second-party ACR tracking that is directly conducted by the\nsmart TV platform. In this work, we conduct a black-box audit of ACR network\ntraffic between ACR clients on the smart TV and ACR servers. We use our\nauditing approach to systematically investigate whether (1) ACR tracking is\nagnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2)\nprivacy controls offered by smart TVs have an impact on ACR tracking, and (3)\nthere are any differences in ACR tracking between the UK and the US. We perform\na series of experiments on two major smart TV platforms: Samsung and LG. Our\nresults show that ACR works even when the smart TV is used as a \"dumb\" external\ndisplay, opting-out stops network traffic to ACR servers, and there are\ndifferences in how ACR works across the UK and the US.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Networking and Internet Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Smart TVs implement a unique tracking approach called Automatic Content
Recognition (ACR) to profile viewing activity of their users. ACR is a
Shazam-like technology that works by periodically capturing the content
displayed on a TV's screen and matching it against a content library to detect
what content is being displayed at any given point in time. While prior
research has investigated third-party tracking in the smart TV ecosystem, it
has not looked into second-party ACR tracking that is directly conducted by the
smart TV platform. In this work, we conduct a black-box audit of ACR network
traffic between ACR clients on the smart TV and ACR servers. We use our
auditing approach to systematically investigate whether (1) ACR tracking is
agnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2)
privacy controls offered by smart TVs have an impact on ACR tracking, and (3)
there are any differences in ACR tracking between the UK and the US. We perform
a series of experiments on two major smart TV platforms: Samsung and LG. Our
results show that ACR works even when the smart TV is used as a "dumb" external
display, opting-out stops network traffic to ACR servers, and there are
differences in how ACR works across the UK and the US.