基于深度学习的数字图像取证技术研究进展

doi:10.11959/j.issn.2096-109x.2021047

Abstract

Abstract:

In the new era of rapid development of internet, where massive forgery images with updated tampering techniques flood into, traditional algorithms are no longer able to deal with the latest multimedia tampering techniques, especially those caused by Deepfake and deep learning techniques.Thus, a universal framework for image forensics including image pre-processing module, feature extraction module and post-processing module designed for specific classification were proposed creatively.Accordingly, the state-of-the-art algorithms were reviewed，and meanwhile the main strength and limitations of current algorithms were generalized.More importantly, the future studies were also listed for advancing the development of digital image forensics.

Key words: digital image forensic, convolution neural network, origin identification, forgery detection

CLC Number:

TP37

Tong QIAO, Hongwei YAO, Binmin PAN, Ming XU, Yanli CHEN. Research progress of digital image forensic techniques based on deep learning[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 13-28.

Figures/Tables 13

References 85

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年份	参考文献	特征类型	检测级别	神经网络类型	研究模型块与主要针对的问题
2017年
2018年
2019年

阈值	局部正确率	全局正确率	精度
Γ_dist=0.7, Γ_conf=0.2	0.712	0.982	64×64
Γ_dist=0.7, Γ_conf=0.0	0.734	0.983	64×64

Research progress of digital image forensic techniques based on deep learning

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