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Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A User-Study

arXiv 2307.10334 ↗First posted 19 Jul 2023cs.CVcited by 1

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Abstract (arXiv)

Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights investigators, that explores the capability of Artificial Intelligence (AI)-based image filters to potentially mitigate the emotional impact of viewing such disturbing content. We tested five different filter styles, both traditional (Blurring and Partial Blurring) and AI-based (Drawing, Colored Drawing, and Painting), and measured their effectiveness in terms of conveying image information while reducing emotional distress. Our findings suggest that the AI-based Drawing style filter demonstrates the best performance, offering a promising solution for reducing negative feelings (-30.38%) while preserving the interpretability of the image (97.19%). Despite the requirement for many professionals to eventually inspect the original images, participants suggested potential strategies for integrating AI filters into their workflow, such as using AI filters as an initial, preparatory step before viewing the original image. Overall, this paper contributes to the development of a more ethically considerate and effective visual environment for professionals routinely engaging with potentially disturbing imagery.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 36 references that are in the map. First posted on arXiv 19 Jul 2023.

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5 of 36 references.

References · 5

  1. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  2. 2017Generative Adversarial Networks: An Overviewcited by 590
  3. 2017Neural Style Transfer: A Reviewcited by 199
  4. 2021StyTr2: Image Style Transfer with Transformerscited by 187
  5. 2022Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Modelscited by 4

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