Splicing detection in color image based on deep learning of wavelet decomposed image

Image splicing is done by duplicate part(s) of an image and pasted in a different image. This basic technique is very common in image forgery therefore it reduced the user confidence in the digital image. The need for a reliable and effective method to detect this type of counterfeiting has been inc...

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Bibliographic Details
Main Authors: Khayeat, Ali Retha Hasoon, Al-Moadhen, Ahmed Abdulhadi, Khayyat, Mustafa Ridha Hassoon
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Color imagery
Decomposition
Deep learning
Digital imaging
Forgery
Image segmentation
Machine learning
Semantic segmentation
Semantics
Splicing
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Summary:Image splicing is done by duplicate part(s) of an image and pasted in a different image. This basic technique is very common in image forgery therefore it reduced the user confidence in the digital image. The need for a reliable and effective method to detect this type of counterfeiting has been increased. Splicing detection in colour image based on Deep Learning of Haar wavelet decomposed image is developed in this work. The colour image is converted to grayscale and decomposed into 1st level (LL1, LH1, HL1, and HH1). Then to detect splicing, we used the semantic segmentation of the decomposed image. Consider that the Convolutional Neural Network model was used to segment the decomposed LL1 image. The SegNet is applied to boost the semantic segmentation and utilize the classification in our proposed approach. The experimental work confirmed the efficiency of the suggested method where the forgery (splicing) detected in 89% of the tested images with a very high percentage of localization.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0027442