End4：面向基于扩散的图像修复检测的端到端去噪扩散方法
End4: End-to-end Denoising Diffusion for Diffusion-Based Inpainting Detection
AI总结：
本文提出端到端去噪扩散检测方法 End4，通过去噪重建对齐潜在空间并利用尺度感知金字塔融合模块增强特征判别力，以检测基于扩散的图像修复伪造，实验证明其泛化性和鲁棒性优于现有方法。
摘要：
扩散模型强大的生成能力显著推动了图像合成领域的发展，既提升了完整图像生成，也增强了基于修复的图像编辑。尽管取得了显著进展，扩散模型也引发了对其被恶意滥用的担忧。然而，现有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相似的修复图像也是如此。为应对这一挑战，我们提出了一种基于端到端去噪扩散（End4）的新型检测方法。具体而言，End4 设计了一个去噪重建模型，以提高重建过程与检测过程潜在空间之间的对齐程度，从而重建出更有利于检测的特征。同时，该方法利用尺度感知的金字塔式融合模块（SPFM），在不同尺度注意力金字塔层的引导下细化局部图像特征，增强特征判别力。此外，为评估修复图像的检测性能，我们构建了一个包含五种不同掩码区域生成图像的综合基准。大量实验表明，我们的 End4 能够有效泛化到未见过的掩码模式，并在各种扰动下保持鲁棒性。我们的代码和数据集将很快发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
