Abstract
The powerful generative capabilities of diffusion models have significantly advanced the field of image synthesis, enhancing both full image generation and inpainting-based image editing. Despite their remarkable advancements, diffusion models also raise concerns about potential misuse for malicious purposes. However, existing approaches struggle to identify images generated by diffusion-based inpainting models, even when similar inpainted images are included in their training data. To address this challenge, we propose a novel detection method based on End-to-end denoising diffusion (End4). Specifically, End4 designs a denoising reconstruction model to improve the alignment degree between the latent spaces of the reconstruction and detection processes, thus reconstructing features that are more conducive to detection. Meanwhile, it leverages a Scale-aware Pyramid-like Fusion Module (SPFM) that refines local image features under the guidance of attention pyramid layers at different scales, enhancing feature discriminability. Additionally, to evaluate detection performance on inpainted images, we establish a comprehensive benchmark comprising images generated from five distinct masked regions. Extensive experiments demonstrate that our End4 effectively generalizes to unseen masking patterns and remains robust under various perturbations. Our code and dataset will be released soon.
Abstract (translated)
扩散模型的强大生成能力在图像合成领域取得了显著进展，不仅提高了完整图像的生成质量，还增强了基于修复（inpainting）的图像编辑技术。尽管这些模型取得了非凡的进步，但也引发了对可能被用于恶意目的的担忧。然而，现有的方法难以识别由基于扩散的修复模型生成的图像，即使训练数据中包含类似的修复后的图像也是如此。为了解决这一挑战，我们提出了一种新的检测方法，即端到端去噪扩散（End4）。具体来说，End4 设计了一个去噪重建模型，以提高重建和检测过程中潜在空间之间的对齐度，从而重构出更有利于检测的特征。同时，它利用了尺度感知金字塔状融合模块（SPFM），在不同尺度下的注意力金字塔层指导下细化局部图像特征，增强了特征的区分性。 为了评估修复图像上的检测性能，我们建立了一个全面的基准测试集，其中包括从五个不同的遮罩区域生成的图像。广泛的实验表明，我们的 End4 方法可以很好地推广到未见过的遮罩模式，并在各种扰动下保持稳健性。我们将很快发布代码和数据集。
URL
https://arxiv.org/abs/2509.13214
