To improve the trustworthiness to assess the digital images by identifying authentic images and tampered images, this work is focused on Copy-Move based image Forgery Detection (CMFD) and classification using Improved Relevance Vector Machine (IRVM). In this paper, Biorthogonal Wavelet Transform with Singular Value Decomposition (BWT-SVD)-based feature extraction is applied to find the image forgery. The proposed method begins with dividing the test images into overlapping blocks, and then Biorthogonal Wavelet Transform (BWT) with Singular Value Decomposition (SVD) applies to extract the feature vector from the blocks. After that, the feature vectors are sorts and the duplicate vectors are identified by the similarity between two successive vectors. The occurrences of clone vectors are identified on the basis of Minkowski distance and the threshold value. Then, similarity criteria result in the existence of forgery in images. To classify images into the category of authentic images or forged images, improved version of Relevance Vector Machine (RVM) uses, which leads to efficiency and accuracy of forged image identification process. Performance of proposed scheme tests by performing experiments on CoMoFoD database. The simulation results show that the proposed IRVM scheme attained high performance when compared with existing Copy-Move based image Forgery Detection schemes in MATLAB environment.

为了通过识别真实图像和篡改图像来提高评估数字图像的可信度，这项工作的重点是基于复制移动的图像伪造检测 (CMFD) 和使用改进相关向量机 (IRVM) 的分类。在本文中，基于奇异值分解的双正交小波变换（BWT-SVD）特征提取被应用于发现图像伪造。所提出的方法首先将测试图像划分为重叠块，然后应用奇异值分解（SVD）的双正交小波变换（BWT）从块中提取特征向量。之后，对特征向量进行排序，并通过两个连续向量之间的相似性来识别重复向量。基于闵可夫斯基距离和阈值识别克隆向量的出现。然后，相似性标准导致图像中存在伪造。为了将图像分为真实图像或伪造图像的类别，使用相关向量机（RVM）的改进版本，从而提高伪造图像识别过程的效率和准确性。通过在 CoMoFoD 数据库上执行实验来测试提议方案的性能。仿真结果表明，与现有的基于复制移动的 MATLAB 环境下的图像伪造检测方案相比，所提出的 IRVM 方案获得了高性能。相关向量机（RVM）使用的改进版本，这导致伪造图像识别过程的效率和准确性。通过在 CoMoFoD 数据库上执行实验来测试提议方案的性能。仿真结果表明，与现有的基于复制移动的 MATLAB 环境下的图像伪造检测方案相比，所提出的 IRVM 方案获得了高性能。相关向量机（RVM）使用的改进版本，这导致伪造图像识别过程的效率和准确性。通过在 CoMoFoD 数据库上执行实验来测试提议方案的性能。仿真结果表明，与现有的基于复制移动的 MATLAB 环境下的图像伪造检测方案相比，所提出的 IRVM 方案获得了高性能。