Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations

• URL: http://arxiv.org/abs/2607.15482v1
• Date: Thu, 16 Jul 2026 21:57:27 GMT
• Title: Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations
• Abstract summary: We show how generative inpainting can improve perturbation-based explanations for images.<n>We achieve photorealistic samples that align better with the original data distribution and enhance explanation quality.
• Score: 6.684502446132073
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• $\f{D^3}$QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection [85.9202830503973]
  Visual autoregressive (AR) models generate images through discrete token prediction.<n>We propose to leverage Discrete Distribution Discrepancy-aware Quantization Error (D$3$QE) for autoregressive-generated image detection.
  arXiv  Detail & Related papers  (2025-10-07T13:02:27Z)
• Edge-Enhanced Vision Transformer Framework for Accurate AI-Generated Image Detection [0.0]
  We propose a hybrid detection framework that combines a fine-tuned Vision Transformer (ViT) with a novel edge-based image processing module.<n>The proposed method is highly suitable for real-world applications in automated content verification and digital forensics.
  arXiv  Detail & Related papers  (2025-08-25T10:30:56Z)
• Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction [13.836238771024254]
  We show that the nonlinear characteristics of domain-specific image dynamics cannot be addressed by simple linear transforms.<n>We propose a method termed Global Deep Curve Estimation to reduce domain-specific mismatch exposure.
  arXiv  Detail & Related papers  (2025-04-14T10:24:57Z)
• A Meaningful Perturbation Metric for Evaluating Explainability Methods [55.09730499143998]
  We introduce a novel approach, which harnesses image generation models to perform targeted perturbation.<n> Specifically, we focus on inpainting only the high-relevance pixels of an input image to modify the model's predictions while preserving image fidelity.<n>This is in contrast to existing approaches, which often produce out-of-distribution modifications, leading to unreliable results.
  arXiv  Detail & Related papers  (2025-04-09T11:46:41Z)
• InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models [10.213390634031049]
  Current IIL methods face two main challenges: a tendency towards overconfidence and difficulty in detecting subtle tampering boundaries.<n>We propose a new paradigm that treats IIL as a conditional mask generation task utilizing diffusion models.<n>Our method, InpDiffusion, utilizes the denoising process enhanced by the integration of image semantic conditions to progressively refine predictions.
  arXiv  Detail & Related papers  (2025-01-06T07:32:12Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Denoising Autoregressive Representation Learning [13.185567468951628]
  Our method, DARL, employs a decoder-only Transformer to predict image patches autoregressively. We show that the learned representation can be improved by using tailored noise schedules and longer training in larger models.
  arXiv  Detail & Related papers  (2024-03-08T10:19:00Z)
• Image Inpainting via Tractable Steering of Diffusion Models [48.16994134964729]
  This paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior.<n>Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs)
  arXiv  Detail & Related papers  (2023-11-28T21:14:02Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses [82.56853587380168]
  We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same. We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters. Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-30T08:23:25Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.