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A real-time image forensics scheme based on multi-domain learning

Abstract

In recent years, researchers have attempted to explore methods for real-time image forgery detection. Many approaches were developed to detect a certain number of image modification methods. There are many limitations in practical application. In this paper, a multi-domain learning convolutional neural network (MDL-CNN) is proposed to overcome this limitation. We extract the periodicity property from the original and modified image. Features of modified image extracted from different datasets are then fed into the neural network in training process. Since the proposed MDL-CNN is trained by different types of tempering datasets, our method can distinguish many types of image modifications. To decrease the computation of proposed scheme, 1 × 1 kernel convolution layer is used in the second convolutional layer of each network. Furthermore, a multi-domain loss function is developed to enhance the recognition ability of in-depth learning features. Experimental evaluation results show that MDL-CNN method can significantly improve the forensic performance.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61602253, 61702226); the Humanities and Social Sciences Projects of the Ministry of Education (No. 18YJC760112); the Chinese Postdoctoral Science Foundation (No. 2018M632229); the Experience Design Frontier Methodology and Technology Innovation Research Project (111Project, No. B18027); the Natural Science Foundation of Jiangsu Province (No. BK20170200); Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (No. KF-2018-03-065); the Fundamental Research Funds for the Central Universities (No. JUSRP11854).

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Correspondence to Tao Zhang.

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Yang, B., Li, Z. & Zhang, T. A real-time image forensics scheme based on multi-domain learning. J Real-Time Image Proc 17, 29–40 (2020). https://doi.org/10.1007/s11554-019-00893-8

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Keywords

  • Multi-domain learning
  • Design of neural network
  • Real-time detection
  • Design of classifiers
  • Image forensic