  In the field of image manipulation localization (IML), the small quantity and
poor quality of existing datasets have always been major issues. A dataset
containing various types of manipulations will greatly help improve the
accuracy of IML models. Images on the internet (such as those on Baidu Tieba's
PS Bar) are manipulated using various techniques, and creating a dataset from
these images will significantly enrich the types of manipulations in our data.
However, images on the internet suffer from resolution and clarity issues, and
the masks obtained by simply subtracting the manipulated image from the
original contain various noises. These noises are difficult to remove,
rendering the masks unusable for IML models. Inspired by the field of change
detection, we treat the original and manipulated images as changes over time
for the same image and view the data generation task as a change detection
task. However, due to clarity issues between images, conventional change
detection models perform poorly. Therefore, we introduced a super-resolution
module and proposed the Manipulation Mask Manufacturer (MMM) framework. It
enhances the resolution of both the original and tampered images, thereby
improving image details for better comparison. Simultaneously, the framework
converts the original and tampered images into feature embeddings and
concatenates them, effectively modeling the context. Additionally, we created
the Manipulation Mask Manufacturer Dataset (MMMD), a dataset that covers a wide
range of manipulation techniques. We aim to contribute to the fields of image
forensics and manipulation detection by providing more realistic manipulation
data through MMM and MMMD. Detailed information about MMMD and the download
link can be found at: the code and datasets will be made available.
