Verilogo: Proactive Phishing Detection via Logo Recognition

Ge Wang, He Liu, Sebastian Becerra, Kai Wang, Serge Belongie, Hovav Shacham and Stefan Savage
CS2011-0969
August 18, 2011

Defending users against fraudulent Websites (i.e., phishing) is a task that is reactive in practice. Blacklists, spam filters, and takedowns all depend on first finding new sites and verifying that they are fraudulent. In this paper we explore an alternative approach that uses a combination of computer-vision techniques to proactively identify likely phishing pages as they are rendered, interactive queries to validate such pages with brand holders, and a single keyboard-entry filter to minimize false positives. We have developed a prototype version of this approach within the Firefox browser and we provide a preliminary evaluation of both the underlying technology (the accuracy and performance of logo recognition in Web pages) as well as its effectiveness in controlled small-scale user studies. While no such approach is perfect, our results demonstrate that this technique offers a significant new capability for minimizing response time in combating a wide range of phishing scams.


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