From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
Summary: arXiv:2411.18084v2 Announce Type: replace-cross
Mobile applications have become integral to our everyday lives, offering convenience and functionality at our fingertips. However, many of these apps utilize deceptive design patterns, often referred to as “dark patterns,” which manipulate user behavior through visual emphasis or linguistic nudging. These patterns can lead users to make unintended choices, raising concerns about ethical design in the digital landscape.
Existing research in the field has primarily relied on manual detection methods. While these approaches provide valuable insights, they are time-consuming and struggle to keep up with the fast-paced evolution of mobile applications. Recent explorations into automated detection methods have emerged, but they face significant limitations. Current systems tend to focus on intra-page patterns, require extensive manual exploration of apps, and lack the flexibility needed to adapt to diverse user interfaces.
Introducing AppRay: A Novel Solution
To address these challenges, we introduce AppRay, an innovative system designed to combine task-oriented app exploration with automated detection of deceptive patterns. AppRay aims to minimize manual effort, broaden the scope of detection, and enhance overall performance. The system operates in two distinct stages:
- Task-Oriented and Random Exploration: The first stage integrates exploration guided by large language models with random exploration techniques. This dual approach captures a wide variety of user interface (UI) states, ensuring a comprehensive understanding of how users interact with apps.
- Detection of Deceptive Patterns: In the second stage, AppRay employs a contrastive learning-based multi-label classifier, supplemented by a rule-based refiner for context-aware detection. This enables the identification of both intra-page and inter-page deceptive patterns, providing a robust detection framework.
Datasets and Experimental Results
As part of this research, we have contributed two significant datasets: AppRay-Tainted-UIs and AppRay-Benign-UIs. These datasets comprise a total of 2,185 instances of deceptive patterns, including 149 intra-page cases, spanning 16 different types of patterns across 876 deceptive and 871 benign user interfaces. Importantly, these datasets maintain the relationships between UIs, offering valuable insights into the dynamics of app design.
Experimental results demonstrate the effectiveness of AppRay in detecting deceptive patterns. The system achieved impressive macro and micro averaged precision scores of 0.92 and 0.85, respectively, as well as recall scores of 0.86 and 0.88. The F1 scores were also noteworthy, reaching 0.89 for macro and 0.85 for micro evaluations. These results indicate a remarkable improvement of 27.14% to 1200% over previous detection methods, highlighting AppRay’s capability to uncover previously unexplored deceptive patterns.
Conclusion
In conclusion, the introduction of AppRay marks a significant advancement in the field of deceptive pattern detection in mobile applications. By integrating task-oriented exploration with automated detection techniques, AppRay not only reduces the manual effort required but also enhances the scope and accuracy of detection. This innovative approach paves the way for a more ethical and user-friendly mobile app ecosystem, ultimately benefiting both developers and users alike.