Predictive and Prescriptive Analysis of Deceptive Patterns

Abstract

Dark patterns in user interfaces are subtle yet manipulative design techniques that deceive users into performing unintended actions, compromising their experience and trust. Our project aimstorevolutionizeuserinteractionbydevelopinganautomatedsystemthatdetects, analyses, and resolves these dark patterns in mobile and web applications. Leveraging Fast Region-based Convolutional Neural Networks (FRCNN), our system identifies deceptive visual elements, while advanced bounding box detection and EAST text identification techniques extract andanalyseUIelementsandtextcontenttopinpointmisleadingmessages. By incorporating text pattern matching, colour brightness analysis, and spatial analysis, we uncover hidden dark patterns and provide users with real-time notifications, fostering transparency and trust. Our approach includes strategies to mitigate and correct thesepatterns, promoting ethical design practices and transforming the digital landscape into a more trustworthy environment. Through rigorous testing and validation, we ensure the reliability and effectiveness of our system, contributing to a safer, more honest user experience.

Introduction

Summary:

The rise of e-commerce has introduced convenience but also unethical practices known as dark patterns—deceptive UI strategies that manipulate users into making unintended decisions, such as accidental purchases or sharing personal data. These patterns challenge transparency, user autonomy, and digital fairness.

Key Contributions of the Research:

1. Problem Overview:

• Traditional detection of dark patterns relies on manual auditing or rule-based systems, which are inefficient and non-scalable.

• Regulatory actions are reactive; AI-driven, real-time solutions are needed.

2. Proposed Framework: AI-Powered Dark Pattern Detection

The framework combines computer vision, natural language processing (NLP), and behavioral analytics to detect and mitigate dark patterns in real time.

A. Visual UI Analysis

• Uses Faster R-CNN and YOLO to identify deceptive UI elements (e.g., hidden buttons, fake urgency prompts).

• Detects visual tricks like disguised ads or misleading calls-to-action.

B. Textual Analysis

• Extracts interface text using the EAST model.

• Classifies text with BERT/RoBERTa to detect manipulative language (e.g., “limited time only”).

C. User Interaction Analytics

• Tracks user behavior (mouse movement, scrolling, hesitation).

• Uses entropy calculation and LSTM models to identify confusion or coercion caused by deceptive design.

3. Real-Time Detection and Alerts

• Combines predictions from all three components into a deception score.

• If the score crosses a threshold, it triggers real-time alerts.

• Deployed as:

  • Browser extension for users

  • API tools for regulators

  • Compliance dashboard for businesses

4. Technical Implementation

• Built using Python, TensorFlow, and OpenCV.

• Uses a dataset of 50,000 annotated UI elements from real-world platforms.

• Achieves high performance (Precision: 91.2%, Recall: 88.7%, F1-score: 89.9%).

5. Societal Impact

• Enhances user protection, promotes ethical UI/UX practices, and supports regulatory enforcement.

• Aims to foster a transparent and fair digital commerce ecosystem.

Conclusion

This research presents a comprehensive framework for detecting deceptive UIpatterns in digital interfaces through a multi-modal deep learning approach. By integrating computer vision, natural language processing, and behavioral analysis, our model achieves state-of-the-art performance in identifying deceptive elements within web and mobile interfaces. The experimental results validate the robustness and reliability of our approach, demonstrating significant improvements over traditional methods.FasterR-CNNemergedas the most effective detectionmodel,outperformingotherarchitecturesinprecision,recall,and F1-score. Our study highlights the critical role of feature importance analysis in understanding deception in UI design, providing valuable insights for regulatory bodies,UXdesigners,and developers. The integration of Grad-CAM-based interpretability methods enables better transparency and accountability in automated detection systems. Moreover, our findings underscore the practical applications of our model, such as browser extensions for real-time deception alerts, API-based auditing tools for regulatory enforcement, and compliance dashboards for e-commerce platforms. These implementations contribute to a more ethical digital ecosystem, protecting users from manipulative practices and fostering fair consumer interactions. Future research should focus on expanding the dataset to include a broader spectrum of deceptive UI patterns and exploring hybrid architecturesthatcombineFasterR-CNNwith transformer-based vision models. Additionally, real-world deployment and longitudinal studies are necessary to evaluate the long-term effectiveness of our system in mitigating deceptive design practices. In conclusion, our work advances the field of UI deception detection by offering a scalable, high-accuracy solution that enhances fairness and transparency in digital interactions. By leveraging deep learning and interpretability techniques, we provide a robust foundation for future innovations in ethical UI design and automated deception mitigation.

References

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Copyright

Copyright © 2025 Arivumathi V, M C Pavan Kumar, Vanish S, Mrs. Dr. K. Periyarselvam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.