Pixels of Faith: Exploiting Visual Saliency to Detect Religious Image Manipulation

Abstract

The proliferation of generative models has revolutionized various aspects of daily life, bringing both opportunities and challenges. This paper tackles a critical problem in the field of religious studies: the automatic detection of partially manipulated religious images. We address the discrepancy between human and algorithmic capabilities in identifying fake images, particularly those visually obvious to humans but challenging for current algorithms. Our study introduces a new testing dataset for religious imagery and incorporates human-derived saliency maps to guide deep learning models toward perceptually relevant regions for fake detection. Experiments demonstrate that integrating visual attention information into the training process significantly improves model performance, even with limited eye-tracking data. This human-in-the-loop approach represents a significant advancement in deepfake detection, particularly for preserving the integrity of religious and cultural content. This work contributes to the development of more robust and human-aligned deepfake detection systems, addressing critical challenges in the era of widespread generative AI technologies.