Image manipulation detection by multiple tampering traces and edge artifact enhancement

Xun Lin, Shuai Wang*, Jiahao Deng, Ying Fu, Xiao Bai, Xinlei Chen, Xiaolei Qu, Wenzhong Tang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

40 引用 (Scopus)

摘要

Image manipulation detection has attracted considerable attention owing to the increasing security risks posed by fake images. Previous studies have proven that tampering traces hidden in images are essential for detecting manipulated regions. However, existing methods have limitations in generalization and the ability to tackle post-processing methods. This paper presents a novel Network to learn and Enhance Multiple tampering Traces (EMT-Net), including noise distribution and visual artifacts. For better generalization, EMT-Net extracts global and local noise features from noise maps using transformers and captures local visual artifacts from original RGB images using convolutional neural networks. Moreover, we enhance fused tampering traces using the proposed edge artifacts enhancement modules and edge supervision strategy to discover subtle edge artifacts hidden in images. Thus, EMT-Net can prevent the risks of losing slight visual clues against well-designed post-processing methods. Experimental results indicate that the proposed method can detect manipulated regions and outperform state-of-the-art approaches under comprehensive quantitative metrics and visual qualities. In addition, EMT-Net shows robustness when various post-processing methods further manipulate images.

源语言英语
文章编号109026
期刊Pattern Recognition
133
DOI
出版状态已出版 - 1月 2023

指纹

探究 'Image manipulation detection by multiple tampering traces and edge artifact enhancement' 的科研主题。它们共同构成独一无二的指纹。

引用此