Abstract
Technological advances in the digital world have led to a tremendous growth in the popularity of digital photography in all walks of life. However, photo editing software tools are easy to use and make photo manipulation a breeze. Therefore, there is a need to find the wrong part of the image. Therefore, this work focuses on finding false images used using the copying process, better known as Copy Move Forgery Detection (CMFD). A copy of Motof spoofing basically means to hide or duplicate a place in a region by attaching certain parts of the same image to it. Initially, digital input images are pre-processed with a Gaussian filter, which is used to blur the image and reduce noise. After further development, a collection of Multi-kernel Fuzzy C-means clustering (MKFCM) was developed to classify images into multiple groups and depending on the various features, the features were extracted using the SIFT algorithm. Finally, with the help of an in-depth reading method, part of the illegal images are found. Test results show that this method is effective and efficient in detecting digital image deception and its functionality and the proposed method is shown in false images.
Erratum to: An improved block-based matching algorithm of copy-move forgery detection
