MTFDN：基于多任务学习的图像复制移动伪造检测方法

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems Pub Date : 2024-09-14 DOI:10.1111/exsy.13729

Peng Liang, Hang Tu, Amir Hussain, Ziyuan Li

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引用次数: 0

摘要

图像复制移动伪造是指在同一图像中复制和粘贴一个图像区域，这是一种简单但却被广泛使用的操作。在本文中，我们从多任务学习的角度重新思考了复制移动伪造检测问题，并总结了该问题的两个特点：(1) 同源性和 (2) 被操纵的痕迹。因此，我们提出了一种用于图像复制移动伪造定位和来源/目标区分的多任务伪造检测网络（MTFDN）。该网络由硬参数共享特征提取器、全局伪造同源检测（GFHD）和局部操纵痕迹检测（LMTD）模块组成。通过共享参数，GFHD 模块和 LMTD 模块之间的特征分布差异显著缩小。在几个基准复制移动伪造数据集上的实验结果证明了我们提出的 MTFDN 的有效性。

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MTFDN: An image copy‐move forgery detection method based on multi‐task learning

Image copy‐move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy‐move forgery detection from the perspective of multi‐task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi‐task forgery detection network (MTFDN) for image copy‐move forgery localization and source/target distinguishment. The network consists of a hard‐parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy‐move forgery datasets demonstrate the effectiveness of our proposed MTFDN.

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来源期刊

Expert Systems 工程技术-计算机：理论方法

CiteScore

7.40

自引率

6.10%

发文量

266

审稿时长

24 months

期刊介绍： Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.