Smudge Attacks on Smartphone Touch Screens

By Adam J. Aviv, Katherine L. Gibson, Evan Mossop, M. Blaze, Jonathan M. Smith. Workshop on Offensive Technologies, 2010

790 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6df7a106ae9e6ffc9aac9671acabdc16a28e0740

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Citations in the corpus

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users (2015, unknown): “scans), and to side channel attacks such as smudge [3], reflection [4], and video capture [5] attacks.”
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication (2012, background): “Recent studies have demonstrated how simple attacks such as smudge attacks [3] can break entry-point authentication schemes.”
You Are How You Touch: User Verification on Smartphones via Tapping Behaviors (2014, methodology): “Exploiting the behavioral information captured by multiple sensors on a smartphone, we can exclusively create a detailed user profile for verifying the owner of the smartphone.”
Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, background): “It has been shown that with special lighting and high-resolution photography, one can easily deduce the secret pattern (see Figure 1) [3].”
The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes (2012, background): “The Android 9-dot screen-lock pattern [42] is a scaled-down variant providing PIN-level security.”
Graphical passwords: Learning from the first twelve years (2012, background): “The Android screen-unlock scheme has been shown to be susceptible to “smudge attacks” [Aviv et al. 2010], where attackers can determine a user’s password through the finger smudges left on the smart phone’s surface.”
Touch me once and i know it's you!: implicit authentication based on touch screen patterns (2012, methodology): “Other attacks include the infamous smudge attack [1], in which finger traces left on the screen are used to extract the password.”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, background): “These personalized attacks represent their own area of focus [2, 17], and while they are of serious importance, combating such threats relies on individual insights that benefit little from largescale measurement.”
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors (2012, background): “[3] studies the feasibility of identifying the password pattern of screen lock by examining the smudges left on touchscreen after usage.”