scholarly journals A Deep Learning based Method for Image Splicing Detection

2021 ◽  
Vol 1714 ◽  
pp. 012038
Author(s):  
Kunj Bihari Meena ◽  
Vipin Tyagi
2020 ◽  
Vol 18 (05) ◽  
pp. 2050024
Author(s):  
Angelina Gokhale ◽  
Mandaar B. Pande ◽  
Dhanya Pramod

In this paper, we present an implementation of quantum transfer learning to blind and passive detection of image splicing forgeries. Though deep learning models are becoming increasingly popular for various computer vision use cases, they depend on powerful classical machines and GPUs for dealing with complex problem solving and also to reduce the time taken for computation. The quantum computing research community has demonstrated elegant solutions to complex use cases in deep learning and computer vision for reducing storage space and increasing the accuracy of results compared to those obtained on a classical computer. We extend the quantum transfer learning approach formerly applied to image classification, for solving the growing problem of image manipulation, specifically, image splicing detection. A hybrid model is built using the ResNet50 pre-trained classical deep learning network and a quantum variational circuit to classify spliced versus authentic images. We present a comparative empirical study of classical versus quantum transfer learning approach using Xanadu’s pennylane quantum simulator and the pytorch deep learning framework. The model was also evaluated on the actual quantum processor ibmqx2 provided by IBM. Results obtained by execution on the quantum processor ([Formula: see text]%, [Formula: see text]%) and simulator ([Formula: see text]%, [Formula: see text]%) showed improvements in comparison to those obtained from classical computers ([Formula: see text]%, [Formula: see text]%).


2021 ◽  
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil

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