Unveiling the Tricks: Automated Detection of Dark Patterns in Mobile Applications

TL;DR

This paper introduces UIGuard, an automated system that uses computer vision and natural language processing to detect dark patterns in mobile app UIs, improving detection speed and coverage over previous methods.

Contribution

UIGuard integrates existing taxonomies into a unified framework, automates dark pattern detection, and demonstrates superior performance and user awareness enhancement.

Findings

Achieved 0.82 precision, 0.77 recall, 0.79 F1 score in dark pattern detection.

Created the first large dataset of benign and malicious UIs for dark pattern analysis.

User study shows increased user knowledge about dark patterns.

Abstract

Mobile apps bring us many conveniences, such as online shopping and communication, but some use malicious designs called dark patterns to trick users into doing things that are not in their best interest. Many works have been done to summarize the taxonomy of these patterns and some have tried to mitigate the problems through various techniques. However, these techniques are either time-consuming, not generalisable or limited to specific patterns. To address these issues, we propose UIGuard, a knowledge-driven system that utilizes computer vision and natural language pattern matching to automatically detect a wide range of dark patterns in mobile UIs. Our system relieves the need for manually creating rules for each new UI/app and covers more types with superior performance. In detail, we integrated existing taxonomies into a consistent one, conducted a characteristic analysis and distilled knowledge from real-world examples and the taxonomy. Our UIGuard consists of two components, Property Extraction and Knowledge-Driven Dark Pattern Checker. We collected the first dark pattern dataset, which contains 4,999 benign UIs and 1,353 malicious UIs of 1,660 instances spanning 1,023 mobile apps. Our system achieves a superior performance in detecting dark patterns (micro averages: 0.82 in precision, 0.77 in recall, 0.79 in F1 score). A user study involving 58 participants further shows that \tool{} significantly increases users' knowledge of dark patterns.