Building UI/UX Dataset for Dark Pattern Detection and YOLOv12x-based Real-Time Object Recognition Detection System

TL;DR

This paper introduces a new dataset of UI/UX screenshots with dark patterns, and develops a YOLOv12x-based system that detects these patterns in real-time with high accuracy, aiding online platform safety.

Contribution

It presents a novel publicly available dataset of dark pattern UI elements and applies transfer learning with YOLOv12x for effective real-time detection.

Findings

Achieved 92.8% detection accuracy (mAP@50).

Maintained 40.5 FPS inference speed.

Dataset supports further dark pattern detection research.

Abstract

With the accelerating pace of digital transformation and the widespread adoption of online platforms, both social and technical concerns regarding dark patterns-user interface designs that undermine users' ability to make informed and rational choices-have become increasingly prominent. As corporate online platforms grow more sophisticated in their design strategies, there is a pressing need for proactive and real-time detection technologies that go beyond the predominantly reactive approaches employed by regulatory authorities. In this paper, we propose a visual dark pattern detection framework that improves both detection accuracy and real-time performance. To this end, we constructed a proprietary visual object detection dataset by manually collecting 4,066 UI/UX screenshots containing dark patterns from 194 websites across six major industrial sectors in South Korea and abroad. The collected images were annotated with five representative UI components commonly associated with dark patterns: Button, Checkbox, Input Field, Pop-up, and QR Code. This dataset has been publicly released to support further research and development in the field. To enable real-time detection, this study adopted the YOLOv12x object detection model and applied transfer learning to optimize its performance for visual dark pattern recognition. Experimental results demonstrate that the proposed approach achieves a high detection accuracy of 92.8% in terms of mAP@50, while maintaining a real-time inference speed of 40.5 frames per second (FPS), confirming its effectiveness for practical deployment in online environments. Furthermore, to facilitate future research and contribute to technological advancements, the dataset constructed in this study has been made publicly available at https://github.com/B4E2/B4E2-DarkPattern-YOLO-DataSet.