From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps

TL;DR

This paper introduces AppRay, an automated system for detecting deceptive dark patterns in mobile apps, combining task-oriented exploration with contrastive learning to improve detection coverage and accuracy.

Contribution

The paper presents AppRay, a novel system that automates detection of intra- and inter-page deceptive patterns using LLM-guided exploration and a multi-label classifier, surpassing prior manual and limited methods.

Findings

AppRay achieves high precision (0.92) and recall (0.88) in detecting dark patterns.

It significantly outperforms prior methods with 27.14% to 1200% improvements.

The system detects previously unexplored deceptive patterns across diverse UI types.

Abstract

Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate user behavior. Existing research largely relies on manual detection, which is time-consuming and cannot keep pace with rapidly evolving apps. Although recent work has explored automated approaches, these methods are limited to intra-page patterns, depend on manual app exploration, and lack flexibility. To address these limitations, we present AppRay, a system that integrates task-oriented app exploration with automated deceptive pattern detection to reduce manual effort, expand detection coverage, and improve performance. AppRay operates in two stages. First, it combines large language model-guided task-oriented exploration with random exploration to capture diverse user interface (UI) states. Second, it detects both intra-page and inter-page deceptive patterns using a contrastive learning-based multi-label classifier augmented with a rule-based refiner for context-aware detection. We contribute two datasets, AppRay-Tainted-UIs and AppRay-Benign-UIs, comprising 2,185 deceptive pattern instances, including 149 intra-page cases, spanning 16 types across 876 deceptive and 871 benign UIs, while preserving UI relationships. Experimental results show that AppRay achieves macro/micro averaged precision of 0.92/0.85, recall of 0.86/0.88, and F1 scores of 0.89/0.85, yielding 27.14% to 1200% improvements over prior methods and enabling effective detection of previously unexplored deceptive patterns.