A Deep Learning based Method for Image Splicing Detection. (January 2021) - Meena, Kunj Bihari; Tyagi, Vipin
Abstract: Image manipulation has become an easy task due to the availability of user-friendly multimedia tools. The images can be manipulated in several ways. Image splicing is one of such image manipulation methods in which two or more images are merged to obtain a single composite image. These manipulated images can be misused to cheat others. This paper proposes a deep learning-based method to detect image splicing in the images. First, the input image is preprocessed using a technique called 'Noiseprint' to get the noise residual by suppressing the image content. Second, the popular ResNet-50 network is used as a feature extractor. Finally, the obtained features are classified as spliced or authentic using the SVM classifier. The experiments performed on the CUISDE dataset show that the proposed method outperforms other existing methods. The proposed method achieves an average classification accuracy of 97.24%.