Deceptive patterns (DPs) in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have become ubiquitous across various digital platforms. While efforts to mitigate DPs have emerged from legal and technical perspectives, a significant gap in usable solutions that empower users to identify and make informed decisions about DPs in real-time remains. In this work, we introduce AutoBot, an automated, deceptive pattern detector that analyzes websites' visual appearances using machine learning techniques to identify and notify users of DPs in real-time. AutoBot employs a two-staged pipeline that processes website screenshots, identifying interactable elements and extracting textual features without relying on HTML structure. By leveraging a custom language model, AutoBot understands the context surrounding these elements to determine the presence of deceptive patterns. We implement AutoBot as a lightweight Chrome browser extension that performs all analyses locally, minimizing latency and preserving user privacy. Through extensive evaluation, we demonstrate AutoBot's effectiveness in enhancing users' ability to navigate digital environments safely while providing a valuable tool for regulators to assess and enforce compliance with DP regulations.

翻译：数字界面中的欺骗性模式利用认知偏差和心理弱点操纵用户做出非本意的决策。这些模式已在各类数字平台中无处不在。尽管从法律和技术角度已出现缓解欺骗性模式的相关努力，但在帮助用户实时识别欺骗性模式并做出知情决策的可用解决方案方面仍存在显著空白。本研究提出AutoBot——一种自动化的欺骗性模式检测器，通过机器学习技术分析网站视觉外观，以实时识别并向用户提示欺骗性模式。AutoBot采用两阶段处理流程：对网站截图进行处理，在不依赖HTML结构的情况下识别可交互元素并提取文本特征。通过定制语言模型，AutoBot能理解元素上下文以判定欺骗性模式的存在。我们将AutoBot实现为轻量级Chrome浏览器扩展，所有分析均在本地执行，从而最大限度降低延迟并保护用户隐私。经广泛评估，我们证明AutoBot能有效提升用户安全浏览数字环境的能力，同时为监管机构评估和执行欺骗性模式法规合规性提供重要工具。