A Novel Approach to Detect Copy Move Forgery Using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images.