AI

Picasso: Lightweight Device Class Fingerprinting for Web Clients

Abstract

In this work we present Picasso: a lightweight device class fingerprinting protocol that allows a server to verify the software and hardware stack of a mobile or desktop client. As an example, Picasso can distinguish between traffic sent by an authentic iPhone running Safari on iOS from an emulator or desktop client spoofing the same configuration. Our fingerprinting scheme builds on unpredictable yet stable noise introduced by a client's browser, operating system, and graphical stack when rendering HTML5 canvases. Our algorithm is resistant to replay and includes a hardware-bound proof of work that forces a client to expend a configurable amount of CPU and memory to solve challenges. We demonstrate that Picasso can distinguish 52 million Android, iOS, Windows, and OSX clients running a diversity of browsers with 100%% accuracy. We discuss applications of Picasso in abuse fighting, including protecting the Play Store or other mobile app marketplaces from inorganic interactions; or identifying login attempts to user accounts from previously unseen device classes.