Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network (ECCV 2018)
Please move to below repository.
https://github.com/plok5308/DJPEG-torch
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Title: Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network
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Conference: The European Conference on Computer Vision (ECCV)
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Data normalization is important. You should set the Y value having 0-255, not 0-1.
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Proposed network tensorflow code.
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Trained weights link: https://drive.google.com/open?id=1HlKSoCGVm_2Gia--MH5Bx3SquJos-kr1.
The network was trained with two dataset.
First dataset was generated using 1120 quantization tables (mentioned in the paper).
Second dataset was generated using standard quantization tables (Q50 - Q100).
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Example code (application.py) is updated now.
normal_galaxy_note8_1.jpg - normal image was captured by galaxy note8 smartphone.
splicing_PQ10.jpg - manpulated image (splicing) was generated by Photoshop.
copy_move_PQ10.jpg - manpulated image (copy-move) was generated by Photoshop.
PQ# means save jpg quality factors in Photoshop.
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You can download single and double JPEG images and 1120 quantization tables at https://sites.google.com/view/jspark/home/djpeg.
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Videos showing the process of generating manipulated images. https://drive.google.com/file/d/1G3fOUIBgI3Th0bjPVvP6Dzba-o_Wx3Ev/view?usp=sharing
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Rebuttal. https://drive.google.com/file/d/1RFLk0b_pilQUYpz82mTFOl0aR-Q-IfSA/view?usp=sharing