Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples

Shiqi Wu, Bo Wang, Jianxiang Zhao, Mengnan Zhao, Kun Zhong, Yanqing Guo

Source Title: International Journal of Digital Crime and Forensics (IJDCF)13(3)

ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799860365|DOI: 10.4018/IJDCF.20210501.oa3

MLA

Wu, Shiqi, et al. "Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples." IJDCF vol.13, no.3 2021: pp.34-46. http://doi.org/10.4018/IJDCF.20210501.oa3

APA

Wu, S., Wang, B., Zhao, J., Zhao, M., Zhong, K., & Guo, Y. (2021). Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples. International Journal of Digital Crime and Forensics (IJDCF), 13(3), 34-46. http://doi.org/10.4018/IJDCF.20210501.oa3

Chicago

Wu, Shiqi, et al. "Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples," International Journal of Digital Crime and Forensics (IJDCF) 13, no.3: 34-46. http://doi.org/10.4018/IJDCF.20210501.oa3

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Abstract

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.