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.

翻译：在人工智能领域，任何希望取得良好结果的模型都离不开大量高质量数据，这在篡改检测领域尤为突出。本文提出一种改进的全变分降噪方法，用于获取高质量的篡改图像。我们通过百度PS吧自动抓取原始图像与篡改图像——该网站汇聚了网友发布的海量篡改图像。通过将原始图像与篡改图像进行差分运算，可凸显篡改区域，但最终结果中仍存在大量噪声，导致这些图像无法直接用于深度学习模型。为此，我们提出的改进全变分降噪方法旨在解决该问题。由于文本通常呈细长状，开闭运算后极易丢失文本信息，因此我们采用MSER（最大稳定极值区域）与NMS（非极大值抑制）技术提取文本信息，随后使用改进的全变分降噪技术处理差分图像。最终通过融合图像与文本信息，获得低噪声图像。该方案还显著保留了文本信息。通过此方法生成的图像数据集可应用于深度学习模型，并有效提升模型性能。