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Poster
Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah · Ali Shafahi · Tom Goldstein

Thu Jul 16 07:00 AM -- 07:45 AM & Thu Jul 16 06:00 PM -- 06:45 PM (PDT) @
in Poster Session 42 »

It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. As proof of concept, we describe a well-known music identification method and implement this system in the form of a neural net. We then attack this system using simple gradient methods and show that it is easily broken with white-box attacks. By scaling these perturbations up, we can create transfer attacks on industrial systems, such as the AudioTag copyright detector and YouTube's Content ID system, using perturbations that are audible but significantly smaller than a random baseline. Our goal is to raise awareness of the threats posed by adversarial examples in this space and to highlight the importance of hardening copyright detection systems to attacks.

Keywords: Adversarial Examples · Other · Applications - Other

Author Information

Parsa Saadatpanah (University of Maryland)
Ali Shafahi (University of Maryland)
Tom Goldstein (University of Maryland)

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