A Passive Approach for Detecting Image Splicing using Deep Learning and Haar Wavelet Transform

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Eman I. Abd El-Latif 1,* Ahmed Taha 2 Hala H. Zayed 2

1. Mathematics Department, Faculty of Science, Benha University, Egypt

2. Computer Science Department, Faculty of Computers and Informatics, Benha University, Egypt

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2019.05.04

Received: 10 Mar. 2019 / Revised: 15 Mar. 2019 / Accepted: 20 Mar. 2019 / Published: 8 May 2019

Index Terms

Splicing Image Forgery, Tampered Image Detection, Convolution Neural Network (CNN), Haar Wavelet Transform (HWT), Discrete Cosine Transform (DCT), Support Vector Machine (SVM)


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.

Cite This Paper

Eman I. Abd El-Latif, Ahmed Taha, Hala H. Zayed,"A Passive Approach for Detecting Image Splicing using Deep Learning and Haar Wavelet Transform", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.5, pp.28-35, 2019. DOI:10.5815/ijcnis.2019.05.04


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