Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations

2026-07-16

AI Summary

This work proposes a novel perturbation-based explainable artificial intelligence (XAI) method that integrates generative image inpainting into the LIME framework to address the limitations of conventional perturbation techniques, which often produce artifacts and distributional shifts that degrade explanation quality. By leveraging generative inpainting, the approach synthesizes perturbed samples that adhere closely to the original data distribution and exhibit high visual fidelity. This effectively eliminates visible artificial traces while preserving local faithfulness, thereby substantially enhancing both the accuracy and credibility of model explanations. The method establishes a more reliable foundation for visual XAI by ensuring that generated perturbations are perceptually realistic and semantically consistent with the input data.

Abstract

The increasing complexity of state-of-the-art machine learning models has made their behavior progressively harder to interpret, spurring rapid advancements in the field of eXplainable Artificial Intelligence (XAI). Among many methods proposed, perturbation-based approaches play a major role. By systematically altering (perturbing) input features, these approaches measure the impact on the model's predictions. For image data, traditional perturbation techniques, often involve replacing pixel values e.g., with a pre-defined color. However, such approaches, but also more refined deterministic techniques, generate unrealistic out-of-distribution samples and often leave visible artifacts, which can mislead the model and compromise explanation quality. In this work, we adjust LIME, a widely used perturbation-based method, to demonstrate how generative inpainting can improve perturbation-based explanations for images. We achieve photorealistic perturbed samples that align better with the original data distribution and enhance explanation quality.

Problem

• perturbation-based XAI
• image inpainting
• photorealistic perturbations
• out-of-distribution samples
• explanation quality

Innovation

• generative inpainting
• photorealistic perturbations
• perturbation-based XAI
• LIME
• out-of-distribution samples