Abstract

Digital images can be manipulated using the latest tools and techniques without leaving any visible traces. Image tampering detection is required to authenticate image validation. It is concluded from previous research that image tampering modifies the texture micropattern in a digital image. Therefore, texture descriptors can be applied to highlight these changes. A texture descriptor–based technique is proposed for detecting both copy-move and splicing forgery. In the proposed method, an RGB image is converted into a YCbCr image and Cb and Cr image components are extracted, as these components are more sensitive to tampering artifacts. Further, a standard deviation (STD) filter and higher-order texture descriptors are applied on Cb and Cr components. The STD filter is used to highlight important details of objects in the image. A support vector machine classifier is used to classify forged and tampered images. Support vector machine (SVM) classifier gives good results on both large- and small-image data sets. The performance is appraised in three online-available, widely used data sets: CASIA v1.0, CASIA v2.0, and Columbia. The proposed method outperforms most of the state-of-the-art methods.

摘要

数字图像可以使用最新的工具和技术进行处理，而不会留下任何可见的痕迹。图像篡改检测是验证图像验证所必需的。从先前的研究中可以得出结论，图像篡改会修改数字图像中的纹理微图案。因此，可以应用纹理描述符来突出这些变化。提出了一种基于纹理描述符的技术来检测复制移动和拼接伪造。在所提出的方法中，将 RGB 图像转换为 YCbCr 图像并提取 Cb 和 Cr 图像分量，因为这些分量对篡改伪影更敏感。此外，标准偏差 (STD) 过滤器和高阶纹理描述符应用于 Cb 和 Cr 分量。STD 过滤器用于突出图像中对象的重要细节。支持向量机分类器用于对伪造和篡改的图像进行分类。支持向量机 (SVM) 分类器在大图像和小图像数据集上都给出了很好的结果。性能在三个在线可用、广泛使用的数据集中进行评估：CASIA v1.0、CASIA v2.0 和 Columbia。所提出的方法优于大多数最先进的方法。