DEFENDING AGAINST DEEP LEARNING-BASED VIDEO FINGERPRINTING ATTACKS WITH ADVERSARIAL EXAMPLES
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Authors
Hayden, Blake A.
Subjects
adversarial examples
defending Tor
website fingerprinting
video fingerprinting
deep fingerprinting
defending anonymity
anonymity
defending Tor
website fingerprinting
video fingerprinting
deep fingerprinting
defending anonymity
anonymity
Advisors
Barton, Armon C.
Date of Issue
2022-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
In an increasingly digital world, online anonymity and privacy is a paramount issue for internet users. Tools like The Onion Router (Tor) offer users anonymous internet browsing. Recently, however, Tor's anonymity has been compromised through fingerprinting, where machine learning models are used to analyze Tor traffic and predict user viewing habits, with some models achieving an accuracy of over 99%. There are defenses for Tor that attempt to prevent fingerprinting, but many are defeated by new techniques that utilize Deep Neural Networks (DNNs). New defenses that are robust against DNNs use adversarial examples to fool the classifier, but those defenses either assume the user has access to the full traffic trace beforehand or require expensive maintenance from Tor servers. In this thesis, we propose Prism, a defense against fingerprinting attacks that uses adversarial examples to fool classifiers in real time. We describe a novel method of adversarial example generation that enables adversarial example creation as input is learned over time. Prism injects these adversarial examples into the Tor traffic stream to prevent DNNs from accurately predicting sites that a user is viewing, even if the DNN is hardened by adversarial training. We show that Prism reduces the accuracy of defended fingerprinting models from over 98% to 0%. We also show that Prism can be implemented entirely on the server side, increasing deployability for users who run Tor on devices without GPUs.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
Sponsors
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.