Abstract: In the field of image manipulation localization (IML), the small quantity and poor quality of existing datasets consistently pose major issues. A dataset that contains various types of manipulations significantly improves the accuracy of IML models. Images available on public forums, such as those in online image modification communities, frequently undergo manipulation with diverse techniques. Researchers create a dataset from these images and greatly enhance the diversity of manipulation types in the data. However, due to resolution and clarity issues, images sourced from the internet often carry noise, which complicates efforts to obtain clean masks by simply subtracting the manipulated image from the original. This noise proves difficult to eliminate, and as a result, the masks remain unusable for IML models. Drawing inspiration from the field of change detection, researchers treat the original and manipulated images as temporal changes of the same image and approach the data generation task as a change detection challenge. Due to clarity differences between images, traditional change detection models perform poorly. To address this, researchers introduce a super-resolution module and propose the Manipulation Mask Manufacturer (MMM) framework, which enhances the resolution of both original and tampered images to facilitate better comparison. At the same time, the framework transforms the original and tampered images into feature embeddings and combines them, effectively capturing the context. Additionally, researchers employ the MMM framework to develop the Manipulation Mask Manufacturer Dataset (MMMD), which encompasses a broad spectrum of manipulation techniques. Through MMM and MMMD, researchers aim to contribute to the fields of image forensics and manipulation detection by supplying more realistic manipulation data.

摘要： 在图像操控定位（IML）领域中，现有数据集的数量少且质量差一直是主要问题。一个包含多种操控类型的数据集将显著提升IML模型的准确性。公共论坛（如在线图像修改社区）中的图像通常经过多种技术操控，从这些图像创建数据集可以大大增加数据中操控类型的多样性。然而，由于分辨率和清晰度问题，从网络获取的图像往往带有噪声，仅仅通过将操控图像与原始图像相减难以获得干净的掩膜。这些噪声难以去除，导致掩膜无法用于IML模型训练。受变化检测领域的启发，将原始图像和操控图像视为同一图像随时间的变化，并将数据生成任务视为变化检测任务。由于图像之间的清晰度差异，传统变化检测模型表现不佳。为了生成高质量数据集，本文引入了超分辨率模块，并提出了“篡改掩膜生成方法”（MMM）框架，该框架通过提升原始图像和篡改图像的分辨率来改善对比效果。同时，该框架将原始图像和篡改图像转换为特征嵌入并进行拼接，有效地建模上下文信息。此外，本文利用MMM框架创建了“篡改掩膜生成数据集”（MMMD），该数据集涵盖了广泛的操控技术。本文希望通过MMM和MMMD提供更真实的操控数据，为图像取证和操控检测领域做出贡献。