Technical Countermeasures for Deceptive user Interfaces: A Comprehensive Detection and Mitigation Framework
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2026
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| _version_ | 1866902151732658176 |
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| author | Nial Rojan Dr. Smita Sanjay Ambarkar Prof. Sangeeta Parshionikar |
| author_facet | Nial Rojan Dr. Smita Sanjay Ambarkar Prof. Sangeeta Parshionikar |
| contents | Dark patterns are the deceptive interface designs that manipulate users into harmful actions. Even though reg- ulators now impose very large fines, t e chnical d e fenses still remain limited: recent studies show a 54.5% coverage gap, with only 31 of 68 known dark-pattern types being detected by the tools available. This is due to three main reasons. The datasets being offered in public are small and narrow, typically a few thousand examples covering at most 15 to 20 pattern types. Even though detectors analyze static screenshots or isolated text, they are not acquainted with multi-step flows l i ke R o ach Motel cancellations and hidden subscriptions. Meanwhile, advanced Al offers more personalized deception and weakens defenses through adversarial attacks on NLP and vision models. This work surveys the existing text-based, visual, multimodal, and conversational detection methods, comparing approaches, datasets, metrics, and limitations. Building on this analysis, it proposes a four-engine framework that integrates multiple variable like DOM, visual, linguistic, and behavioral signals, and outlines a 10,000-example, multi-platform dataset aligned with a 245-pattern ontology. Finally, it sketches privacy-preserving and adversarially robust deployment strategies for real-time protection. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20393375 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | Technical Countermeasures for Deceptive user Interfaces: A Comprehensive Detection and Mitigation Framework Nial Rojan Dr. Smita Sanjay Ambarkar Prof. Sangeeta Parshionikar dark patterns deceptive interfaces machine learning user journey tracking adversarial robustness privacy-preserving countermeasures Dark patterns are the deceptive interface designs that manipulate users into harmful actions. Even though reg- ulators now impose very large fines, t e chnical d e fenses still remain limited: recent studies show a 54.5% coverage gap, with only 31 of 68 known dark-pattern types being detected by the tools available. This is due to three main reasons. The datasets being offered in public are small and narrow, typically a few thousand examples covering at most 15 to 20 pattern types. Even though detectors analyze static screenshots or isolated text, they are not acquainted with multi-step flows l i ke R o ach Motel cancellations and hidden subscriptions. Meanwhile, advanced Al offers more personalized deception and weakens defenses through adversarial attacks on NLP and vision models. This work surveys the existing text-based, visual, multimodal, and conversational detection methods, comparing approaches, datasets, metrics, and limitations. Building on this analysis, it proposes a four-engine framework that integrates multiple variable like DOM, visual, linguistic, and behavioral signals, and outlines a 10,000-example, multi-platform dataset aligned with a 245-pattern ontology. Finally, it sketches privacy-preserving and adversarially robust deployment strategies for real-time protection. |
| title | Technical Countermeasures for Deceptive user Interfaces: A Comprehensive Detection and Mitigation Framework |
| topic | dark patterns deceptive interfaces machine learning user journey tracking adversarial robustness privacy-preserving countermeasures |
| url | https://doi.org/10.5281/zenodo.20393375 |