Image-guided topic modeling for interpretable privacy classification

• URL: http://arxiv.org/abs/2409.18674v1
• Date: Fri, 27 Sep 2024 12:02:28 GMT
• Title: Image-guided topic modeling for interpretable privacy classification
• Authors: Alina Elena Baia, Andrea Cavallaro,
• Abstract summary: We propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We use the ITM-generated descriptors to learn a privacy predictor, Priv$times$ITM, whose decisions are interpretable by design.
• Score: 27.301741710016223
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We generate descriptors with our novel Image-guided Topic Modeling (ITM) approach. ITM leverages, via multimodality alignment, both vision information and image textual descriptions from a vision language model. We use the ITM-generated descriptors to learn a privacy predictor, Priv$\times$ITM, whose decisions are interpretable by design. Our Priv$\times$ITM classifier outperforms the reference interpretable method by 5 percentage points in accuracy and performs comparably to the current non-interpretable state-of-the-art model.

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