论文标题
图像操纵检测和本地化的对象形式
ObjectFormer for Image Manipulation Detection and Localization
论文作者
论文摘要
图像编辑技术的最新进展对多媒体数据的可信度提出了严重的挑战，该数据推动了图像篡改检测的研究。在本文中，我们建议对象形式检测和本地化图像操作。为了捕获RGB域不再可见的微妙操纵轨迹，我们提取图像的高频特征，并将它们与RGB功能结合在一起，作为多模式贴片嵌入。此外，我们使用一组可学习的对象原型作为中级表示形式，以建模不同区域之间的对象级的一致性，这些区域进一步用于完善补丁嵌入以捕获补丁级的一致性。我们在各种数据集上进行了广泛的实验，结果验证了所提出的方法的有效性，超过了最先进的篡改检测和定位方法。
Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.
