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Cellular automata-based CMF detection under single and multiple post-processing attacks
Multimedia Systems ( IF 3.5 ) Pub Date : 2021-07-02 , DOI: 10.1007/s00530-021-00828-z
Gulnawaz Gani 1 , Zubair Jeelani 1 , Fasel Qadir 1
Affiliation  

Image forgeries created using easy-to-use image editing tools like Adobe Photoshop have become prime sources of fake news, and are often used in malevolent ways in politics, courtrooms, and in scientific publishing as well. To reduce the problems caused by manipulated images in these domains, it is important to have reliable and fast mechanisms in place for verifying their authenticity before their consumption. Automated detection of image forgeries, is, therefore an ongoing challenge for the research community. One of a commonly used image forgeries is Copy-Move Forgery (CMF), where a region of an image is copied and pasted to some other location within the same image to alter its contents. To detect copy-move forgeries, several Copy Move Forgery Detection (CMFD) techniques have been proposed in the past. However, existing techniques are highly sensitive to post-processing attacks such as noise and compression. In this paper, we propose a new CMFD method in which Cellular Automata (CA) is used for feature extraction. The method works by dividing the input image into overlapping blocks and extracting features from these blocks using the CA inversion procedure. Then, the repeated regions are identified by matching the set of extracted feature vectors. The experimental results show that the detection under noise and compression attacks is accurate compared to many existing methods. As such, the proposed approach may be useful for a variety of CMFD scenarios and may have implications for researchers in related fields.



中文翻译:

单次和多次后处理攻击下基于元胞自动机的CMF检测

使用 Adob​​e Photoshop 等易于使用的图像编辑工具创建的图像伪造已成为虚假新闻的主要来源,并且经常以恶意方式用于政治、法庭和科学出版。为了减少这些领域中由操纵图像引起的问题,重要的是要有可靠和快速的机制来在消费前验证其真实性。因此,图像伪造的自动检测是研究界面临的持续挑战。一种常用的图像伪造是复制-移动伪造 (CMF),其中图像的一个区域被复制并粘贴到同一图像内的某个其他位置以更改其内容。为了检测复制移动伪造,过去已经提出了几种复制移动伪造检测(CMFD)技术。然而,现有技术对噪声和压缩等后处理攻击高度敏感。在本文中,我们提出了一种新的 CMFD 方法,其中使用元胞自动机 (CA) 进行特征提取。该方法的工作原理是将输入图像划分为重叠块并使用 CA 反演过程从这些块中提取特征。然后,通过匹配提取的特征向量集来识别重复区域。实验结果表明,与许多现有方法相比,噪声和压缩攻击下的检测是准确的。因此,所提出的方法可能对各种 CMFD 场景有用,并且可能对相关领域的研究人员产生影响。我们提出了一种新的 CMFD 方法,其中使用元胞自动机 (CA) 进行特征提取。该方法通过将输入图像划分为重叠块并使用 CA 反演过程从这些块中提取特征来工作。然后,通过匹配提取的特征向量集来识别重复区域。实验结果表明,与许多现有方法相比,噪声和压缩攻击下的检测是准确的。因此,所提出的方法可能对各种 CMFD 场景有用,并且可能对相关领域的研究人员产生影响。我们提出了一种新的 CMFD 方法,其中使用元胞自动机 (CA) 进行特征提取。该方法通过将输入图像划分为重叠块并使用 CA 反演过程从这些块中提取特征来工作。然后,通过匹配提取的特征向量集来识别重复区域。实验结果表明,与许多现有方法相比,噪声和压缩攻击下的检测是准确的。因此,所提出的方法可能对各种 CMFD 场景有用,并且可能对相关领域的研究人员产生影响。通过匹配提取的特征向量集来识别重复区域。实验结果表明,与许多现有方法相比,噪声和压缩攻击下的检测是准确的。因此,所提出的方法可能对各种 CMFD 场景有用,并且可能对相关领域的研究人员产生影响。通过匹配提取的特征向量集来识别重复区域。实验结果表明,与许多现有方法相比,噪声和压缩攻击下的检测是准确的。因此,所提出的方法可能对各种 CMFD 场景有用,并且可能对相关领域的研究人员产生影响。

更新日期:2021-07-04
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