The Optimal Model for Copy-Move Forgery Detection in Medical Images.

IF 1.1 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical Signals & Sensors Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI:10.4103/jmss.jmss_35_22

Ehsan Amiri, Ahmad Mosallanejad, Amir Sheikhahmadi

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引用次数: 0

Abstract

Background: Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery.

Methods: The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated.

Results: The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images.

Conclusions: It concluded that our method for CMFD in the medical images was more accurate.

Abstract Image

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医学影像中复制移动伪造检测的最佳模型。

背景介绍数字设备可以轻易伪造医学图像。医学图像中的移动复制伪造检测（CMFD）在无法使用先进医疗设备的地区被滥用。复制移动图像的伪造会直接影响医生的决定。本文讨论的方法是检测医学图像伪造的最佳方法：所提出的方法基于进化算法，能很好地检测出伪造块。在第一阶段，借助离散余弦变换（DCT）将图像提取到信号级。然后通过离散小波变换（DWT）进行分割。DWT 的低低频段具有最多的图像属性，被划分为多个区块。使用均衡优化算法搜索每个区块。结果：根据精确度、召回率和 F1 三个标准对提出的方法进行了评估，结果分别为 90.07%、92.34% 和 91.56%。它优于在医学图像上研究的方法：结论：我们的方法对医学图像中的 CMFD 更为准确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-

CiteScore

2.30

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.