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

arXiv: https://arxiv.org/abs/2308.05898 · HTML: https://mlsanity.com/paper/2308.05898/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2023.

## Built on by (most cited first, 9 of 9)

- [SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents](https://mlsanity.com/paper/2510.11035/) (2025)
- [Agent for User: Testing Multi-User Interactive Features in TikTok](https://mlsanity.com/paper/2504.15474/) (2025)
- [GUI Agents with Reinforcement Learning: Toward Digital Inhabitants](https://mlsanity.com/paper/2604.27955/) (2026)
- [Automatically Detecting Online Deceptive Patterns](https://mlsanity.com/paper/2411.07441/) (2024)
- [Breaking Single-Tester Limits: Multi-Agent LLMs for Multi-User Feature Testing](https://mlsanity.com/paper/2506.17539/) (2025)
- [Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces](https://mlsanity.com/paper/2605.09497/) (2026)
- [From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps](https://mlsanity.com/paper/2411.18084/) (2024)
- [On the Suitability of LLM-Driven Agents for Dark Pattern Audits](https://mlsanity.com/paper/2603.03881/) (2026)
- [AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations](https://mlsanity.com/paper/2608.21841/) (2026)

## Abstract (arXiv)

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.