Developing a Web-based Tool for Detecting Deceptive Designs in Cookie Banners

DOI

10.2991/978-94-6463-684-0_6How to use a DOI?

Keywords

cookie banners; dark patterns; deceptive designs; machine learning; random forest classifier; vision transformer

Abstract

Deceptive designs, also known as dark patterns, are user interface tricks websites and applications use to manipulate user behaviour and collect data without informed consent. These patterns include misleading language, asymmetrical options, and hidden information. Websites commonly manifest these deceptive designs in cookie banners to track user behavior. Given the sensitive nature of the data cookies may contain, it is crucial to flag and address potential manipulations of user consent through deceptive designs in cookie banners. This study builds on Ariadne, a browser extension developed by Adorna et al. that identifies deceptive patterns in cookie banners. To improve on their work, this study presents VeraCookie, a web application that has similar functionality but is compatible with all browsers and devices. This application integrates advanced machine learning models, including a Random Forest Classifier for assessing language clarity and a Vision Transformer (ViT) model for evaluating the symmetry of the options present in a cookie banner. Results show that VeraCookie outperforms the previous tool, achieving higher accuracy and better user experience. This study aims to demonstrate the effectiveness of VeraCookie in defense against deceptive designs in cookie banners.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Braullo Jose A. Jo
AU  - Shanea J. Olino
AU  - Ligaya Leah Figueroa
AU  - Ma. Rowena C. Solamo
AU  - Rommel P. Feria
PY  - 2025
DA  - 2025/04/30
TI  - Developing a Web-based Tool for Detecting Deceptive Designs in Cookie Banners
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2024)
PB  - Atlantis Press
SP  - 85
EP  - 107
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-684-0_6
DO  - 10.2991/978-94-6463-684-0_6
ID  - Jo2025
ER  -