A Passive Approach for Detecting Image Splicing Based on Deep Learning and Wavelet Transform

Abstract

Splicing image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. Many algorithms have already been executed on the image splicing. The existing algorithms may be affected by some problems, such as high feature dimensionality and low accuracy with high false positive rates. In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image. In the deep learning approach, convolutional neural network (CNN) is employed to automatically extract features from the spliced image. CNN is applied and then discrete wavelet transform (DWT) is used. Support vector machine is used later for classification. Additional experiments are performed. That is, discrete cosine transform replaces DWT and then principal component analysis is applied. The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0). It achieves high accuracy while using a relatively low-dimensional feature vector. Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.