SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting

• URL: http://arxiv.org/abs/2502.06593v2
• Date: Thu, 22 May 2025 18:13:28 GMT
• Title: SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting
• Authors: Paschalis Giakoumoglou, Dimitrios Karageorgiou, Symeon Papadopoulos, Panagiotis C. Petrantonakis,
• Abstract summary: SAGI-D is the largest and most diverse dataset of AI-generated inpaintings.<n>Our experiments show that semantic alignment significantly improves image quality and aesthetics.<n>Using SAGI-D for training several image forensic approaches increases in-domain detection performance on average by 37.4%.
• Score: 11.216906046169683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in generative AI have made text-guided image inpainting -- adding, removing, or altering image regions using textual prompts -- widely accessible. However, generating semantically correct photorealistic imagery, typically requires carefully-crafted prompts and iterative refinement by evaluating the realism of the generated content - tasks commonly performed by humans. To automate the generative process, we propose Semantically Aligned and Uncertainty Guided AI Image Inpainting (SAGI), a model-agnostic pipeline, to sample prompts from a distribution that closely aligns with human perception and to evaluate the generated content and discard one that deviates from such a distribution, which we approximate using pretrained Large Language Models and Vision-Language Models. By applying this pipeline on multiple state-of-the-art inpainting models, we create the SAGI Dataset (SAGI-D), currently the largest and most diverse dataset of AI-generated inpaintings, comprising over 95k inpainted images and a human-evaluated subset. Our experiments show that semantic alignment significantly improves image quality and aesthetics, while uncertainty guidance effectively identifies realistic manipulations - human ability to distinguish inpainted images from real ones drops from 74% to 35% in terms of accuracy, after applying our pipeline. Moreover, using SAGI-D for training several image forensic approaches increases in-domain detection performance on average by 37.4% and out-of-domain generalization by 26.1% in terms of IoU, also demonstrating its utility in countering malicious exploitation of generative AI. Code and dataset are available at https://github.com/mever-team/SAGI

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