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Image Splicing Forgery Detection Using DCT Coefficients with Multi-Scale LBP

机译:使用多尺度LBP的DCT系数进行图像拼接伪造检测

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摘要

Image forensics is an active research area due to the large number of shared images online. These images can be easily manipulated with advanced image editing tools and the changes cannot be captured easily by bare human eyes. In this paper, a novel model is proposed based on features extracted from DCT coefficients and Multi-Scale LBP image transform to blindly detect image splicing, where two or more images are combined into one. The experiments were performed on two publicly available datasets CASIA v.1.0 and v2.0. Using k-fold cross validation, several performance measures were computed and compared with other state-of-the-art techniques. The proposed technique has demonstrated improved performance with more than 97.3% accuracy and 0.99 area under the ROC curve.
机译:由于大量在线共享图像,图像取证是一个活跃的研究领域。可以使用高级图像编辑工具轻松操作这些图像,并且肉眼无法轻松捕获更改。本文提出了一种新的模型,该模型基于从DCT系数中提取的特征和多尺度LBP图像变换来盲检测图像拼接,其中将两个或更多图像组合为一个模型。实验是在两个公共可用的数据集CASIA v.1.0和v2.0上进行的。使用k倍交叉验证,可以计算出几种性能指标并将其与其他最新技术进行比较。所提出的技术已经证明了改进的性能,其ROC曲线下的精度超过97.3%,面积达到0.99。

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