Passive image forgery detection has attracted
many researchers in the recent years. Image manipulation
becomes easier than before because of the fast
development of digital image editing software. Image
splicing is one of the most widespread methods for
tampering images. Research on detection of image
splicing still carries great challenges. 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, Convolution Neural Network
(CNN) is employed to automatically extract features from
the spliced image. CNN is applied and then Haar Wavelet
Transform (HWT) is used. Support Vector Machine
(SVM) is used later for classification. Additional
experiments are performed. That is, Discrete Cosine
Transform (DCT) replaces HWT and then Principle
Component Analysis (PCA) 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
dimension feature vector. Our results demonstrate that the
proposed algorithm is effective and accomplishes better
performance for detecting the spliced image.