Exposing computer generated images by using deep convolutional neural networks
作者：
Highlights：
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• Proposal of a new approach based on DNN and transfer learning that achieves an accuracy of 0.97.
• Use of an extended dataset (more difficult for the task).
• Faster method proved by reducing execution time in more than ten times.
• Evaluation of different kinds of classifiers in association with a DNN.
• Qualitative analysis of bottleneck features produced by ResNet-50 in CG image detection problem.
摘要
•Proposal of a new approach based on DNN and transfer learning that achieves an accuracy of 0.97.•Use of an extended dataset (more difficult for the task).•Faster method proved by reducing execution time in more than ten times.•Evaluation of different kinds of classifiers in association with a DNN.•Qualitative analysis of bottleneck features produced by ResNet-50 in CG image detection problem.
论文关键词：Digital forensics,CG detection,Deep learning,Transfer learning,Fake news
论文评审过程：Received 29 November 2017, Revised 7 February 2018, Accepted 6 April 2018, Available online 24 April 2018, Version of Record 17 June 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.04.006
