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DARK PATTERN FINDER FOR CONSUMER PROTECTION WEB EXTENSION

Madhusree M, Mrs. Manimegala M, Sangeetha M, Sanmathi Priya K S

Abstract


The rapid growth of e-commerce platforms and web-based services has increased the use of user interface techniques designed to influence online decision-making. Among these techniques, dark patterns are deceptive or manipulative design practices that can mislead users, encourage unintended actions, or make it difficult to understand and control their choices. Identifying such patterns manually across different websites is time-consuming and requires considerable awareness of deceptive interface designs. To address this challenge, this project titled “Dark Pattern Finder” presents an intelligent browser-based system that automatically detects and identifies potential dark patterns present in web pages.The proposed system utilizes Artificial Intelligence and machine learning techniques to analyze webpage content and identify suspicious interface patterns. A browser extension collects relevant webpage information, including DOM elements, visible text, user interactions, and other behavioral evidence. The collected information is processed by a backend detection system developed using Python and FastAPI. A machine learning-based classification model analyzes the extracted content and categorizes potential dark patterns such as scarcity and urgency, deceptive messages, forced actions, and other manipulative interface practices. The system also provides confidence scores and supporting evidence to improve the transparency and interpretability of the detection results.To enhance the reliability of detection, the proposed system integrates multiple analysis approaches, including DOM-based analysis, behavioral observation, and state-change information. The detected patterns are presented directly through the browser extension using visual overlays and user-friendly notifications, enabling users to understand potentially manipulative elements while browsing. The system can also associate detected patterns with relevant legal and regulatory information to provide additional awareness regarding potentially deceptive practices.


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References


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