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Mobile apps bring us many conveniences, such as online shopping andcommunication, but some use malicious designs called dark patterns to trickusers into doing things that are not in their best interest. Many works havebeen done to summarize the taxonomy of these patterns and some have tried tomitigate the problems through various techniques. However, these techniques areeither time-consuming, not generalisable or limited to specific patterns. Toaddress these issues, we propose UIGuard, a knowledge-driven system thatutilizes computer vision and natural language pattern matching to automaticallydetect a wide range of dark patterns in mobile UIs. Our system relieves theneed for manually creating rules for each new UI/app and covers more types withsuperior performance. In detail, we integrated existing taxonomies into aconsistent one, conducted a characteristic analysis and distilled knowledgefrom real-world examples and the taxonomy. Our UIGuard consists of twocomponents, Property Extraction and Knowledge-Driven Dark Pattern Checker. Wecollected the first dark pattern dataset, which contains 4,999 benign UIs and1,353 malicious UIs of 1,660 instances spanning 1,023 mobile apps. Our systemachieves a superior performance in detecting dark patterns (micro averages:0.82 in precision, 0.77 in recall, 0.79 in F1 score). A user study involving 58participants further shows that \tool{} significantly increases users'knowledge of dark patterns.

Unveiling the Tricks: Automated Detection of Dark Patterns in Mobile Applications