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BSTRACT Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation. Machine learning approaches have been proposed to enhance detection accuracy, yet aspects of practical deployment have frequently been overlooked.

Google Scholar Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan Mayer, Arvind Narayanan, and Edward W. Felten. Google Scholar Digital Library Georg Merzdovnik, Markus Huber, Damjan Buhov, Nick Nikiforakis, Sebastian Neuner, Martin Schmiedecker, and Edgar Weippl. Google Scholar Cross Ref Kunlun Ren, Weizhong Qiang, Yueming Wu, Yi Zhou, Deqing Zou, and Hai Jin.

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