# From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps

arXiv: https://arxiv.org/abs/2411.18084 · HTML: https://mlsanity.com/paper/2411.18084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 77 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 25 of 25)

- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation](https://mlsanity.com/paper/2312.03003/) (2023)
- [Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware Decisions](https://mlsanity.com/paper/2310.15780/) (2023)
- [Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?](https://mlsanity.com/paper/2008.05132/) (2020)
- [Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence](https://mlsanity.com/paper/2001.02479/) (2020)
- [Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning](https://mlsanity.com/paper/2003.00380/) (2020)
- [Owl Eyes: Spotting UI Display Issues via Visual Understanding](https://mlsanity.com/paper/2009.01417/) (2020)
- [GPTVoiceTasker: Advancing Multi-step Mobile Task Efficiency Through Dynamic Interface Exploration and Learning](https://mlsanity.com/paper/2401.14268/) (2024)
- [AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces](https://mlsanity.com/paper/2303.06782/) (2023)
- [Unveiling the Tricks: Automated Detection of Dark Patterns in Mobile Applications](https://mlsanity.com/paper/2308.05898/) (2023)
- [I am Definitely Manipulated, Even When I am Aware of it. It s Ridiculous! -- Dark Patterns from the End-User Perspective](https://mlsanity.com/paper/2104.12653/) (2021)
- [Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective](https://mlsanity.com/paper/2009.10194/) (2020)
- [MadDroid: Characterising and Detecting Devious Ad Content for Android Apps](https://mlsanity.com/paper/2002.01656/) (2020)
- [The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating](https://mlsanity.com/paper/2204.03200/) (2022)
- [I Want My App That Way: Reclaiming Sovereignty Over Personal Devices](https://mlsanity.com/paper/2102.11819/) (2021)
- [Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps](https://mlsanity.com/paper/2308.09029/) (2023)
- [GreaseVision: Rewriting the Rules of the Interface](https://mlsanity.com/paper/2204.03731/) (2022)
- [Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps](https://mlsanity.com/paper/2304.12811/) (2023)
- [Getting Trapped in Amazon's "Iliad Flow": A Foundation for the Temporal Analysis of Dark Patterns](https://mlsanity.com/paper/2309.09635/) (2023)
- [What Aspects of Mobile Ads Do Users Care About? An Empirical Study of Mobile In-app Ad Reviews](https://mlsanity.com/paper/1702.07681/) (2017)
- [Do Users Care about Ad's Performance Costs? Exploring the Effects of the Performance Costs of In-App Ads on User Experience](https://mlsanity.com/paper/2010.16063/) (2020)

## Abstract (arXiv)

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.