• 论文 •

### 基于深度学习的图像拼接篡改检测

1. 1. 北京航空航天大学 电子信息工程学院, 北京 100083;
2. 中国人民公安大学 信息技术与网络安全学院, 北京 100038
• 收稿日期:2019-11-13 发布日期:2020-05-19
• 通讯作者: 边亮 E-mail:askquestionbl@163.com
• 作者简介:边亮 男,硕士研究生。主要研究方向:图像信息融合与处理;罗霄阳 男,硕士研究生。主要研究方向:机器学习、图像取证;李硕 男,硕士研究生。主要研究方向:视频图像侦查。

### Image mosaic tampering detection based on deep learning

BIAN Liang1, LUO Xiaoyang2, LI Shuo2

1. 1. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China;
2. School of Information Technology and Network Security, People's Public Security University of China, Beijing 100038, China
• Received:2019-11-13 Published:2020-05-19

Abstract: The traditional image stitching detection algorithm manually constructs the stitching features by researchers. With the advancement of technology and the continuous development of image processing technology, the limitations of the features of manual construction, such as weak robustness and difficult positioning, are gradually manifested. Aimed at this kind of problem, this paper proposes to construct a Convolutional Neural Network (CNN) by means of fixed pre-convolution kernel, and detect the image tampering area by feature self-learning. Through experiments and research, it is found that the features of the mosaic tampering area of the spliced tamper image can be learned by the CNN model. Prior to the CNN model, the convolution kernel uses a high-pass filter and the activation function uses an Exponential Linear Unit (ELU), which makes the CNN model be capable of identifying features such as splicing and tampering with image edge traces. The detection results show that the positioning accuracy for the falsification image’s tampering area is 84.3% in the IEEE IFS-TC image forensics training set and the detection true negative rate of the tampering area is 96.18%.