DPMSN: A Dual-Pathway Multiscale Network for Image Forgery Detection

稳健性(进化) 计算机科学 计算机视觉 RGB颜色模型 人工智能 边缘检测 GSM演进的增强数据速率 卷积(计算机科学) 图像(数学) 数字图像 模式识别(心理学) 图像处理 人工神经网络 生物化学 化学 基因
作者
Nianyin Zeng,Peishu Wu,Yuqing Zhang,Han Li,Jingfeng Mao,Zidong Wang
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (5): 7665-7674 被引量:31
标识
DOI:10.1109/tii.2024.3359454
摘要

Multimedia images have become an important way for the sharing of digital information. However, advanced editing tools provide easy methods for malicious content modification, resulting in less viable information. Therefore, there is an urgent need to design an algorithm for image tampering detection and localization, which is capable of locating the authenticity region of the received image, thus delivering the accurate information and assisting in correct decision making in industries and other fields. In this article, a novel dual-pathway multiscale network (DPMSN) is proposed for the image forgery detection, which mainly focuses on extracting the edge information. In particular, a dual-pathway structure is deployed to align visual features in red, green and blue (RGB) space and edge information in LAB space, where a coarse prediction mask is generated to promote accurate localization of the forged regions. By applying the variation convolution operators, comprehensive attention can be paid to various forged regions in multiple sizes. Moreover, in the multiscale fusion module, features at different stages and other low-level information are sufficiently fused to realize a robust presentation of the forged regions. Experimental results show the effectiveness of DPMSN as compared with other state-of-the-art image forgery detection models and the great robustness when facing image attacks, which means DPMSN is a trustworthy forgery detection approach in the industrial field.
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