Image Forgery and Image Tampering Detection Techniques: A Review

Abstract

AbstractImage forgery is the process of manipulating digital images to obscure critical information or details for personal or business gain. Nowadays, tampering and forging digital images have become frequent and easy due to the emergence of effective photo editing software and high-definition capturing equipment. Thus, several image forgery detection techniques have been developed to guarantee the authenticity and legitimacy of the images. There are several types of image forgery techniques; among them, copy-move forgery is the most common one. The paper discusses the types of image forgery and the methods proposed to detect or localize them, including Principal Component Analysis (PCA), DCT, CNN models like Encoder-Decoder, SVGGNet, MobileNet, VGG16, Resnet50, and clustering models like BIRCH. A comparison of different detection techniques is performed, and their results are also observed. Image Forgery Detection in the Medical Field is a significant area for smart health care.KeywordsCopy-move image forgerySVGGNetMobileNet-V2Balanced iterative reducing and clustering using hierarchies (BIRCH)Support vector machines (SVM)Encoder-decoderConvolutional neural network (CNN)Convolutional neural network with error level analysis (CNN_ELA)Convolutional neural network with error level analysis and sharpening pre-processing techniques (CNN_SHARPEN_ELA)Principal component analysis (PCA)Discrete cosine transform (DCT)