Manipulation Mask Generator: High-Quality Image Manipulation Mask
  Generation Method Based on Modified Total Variation Noise Reduction
Xinyu Yang Jizhe Zhou
In artificial intelligence, any model that wants to achieve a good result is
inseparable from a large number of high-quality data. It is especially true in
the field of tamper detection. This paper proposes a modified total variation
noise reduction method to acquire high-quality tampered images. We
automatically crawl original and tampered images from the Baidu PS Bar. Baidu
PS Bar is a website where net friends post countless tampered images.
Subtracting the original image with the tampered image can highlight the
tampered area. However, there is also substantial noise on the final print, so
these images can't be directly used in the deep learning model. Our modified
total variation noise reduction method is aimed at solving this problem.
Because a lot of text is slender, it is easy to lose text information after the
opening and closing operation. We use MSER (Maximally Stable Extremal Regions)
and NMS (Non-maximum Suppression) technology to extract text information. And
then use the modified total variation noise reduction technology to process the
subtracted image. Finally, we can obtain an image with little noise by adding
the image and text information. And the idea also largely retains the text
information. Datasets generated in this way can be used in deep learning
models, and they will help the model achieve better results.