Implementation of a quantum transfer learning approach to image splicing detection

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]%).

Measurement ◽  
2021 ◽  
pp. 109953
Author(s):  
Adhiyaman Manickam ◽  
Jianmin Jiang ◽  
Yu Zhou ◽  
Abhinav Sagar ◽  
Rajkumar Soundrapandiyan ◽  
...  

2021 ◽  
Author(s):  
Muhammad Sajid

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.


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

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