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Distinguishing between photographic images (PG) and computer-generated (CG) images is a difficult task. In this work, we construct an effective nerual network for this fundamental image forensic problem.

191578010/TADA-NET

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TADA-NET

[1]Joint Learning of deep texture and high frequency features for CG images detection.

[2]Trace aware dual attention network.

More Codes and the KGRA-DATASET will be released soon.

Some CG images in KGRA dataset:

ee45 ee47 ee42 dd44 bb651 4432 48 331 gg39

Some PG images in KGRA dataset:

PG 15 PG 27 PG 28 PG 26 PG 77 PG 78 PG 83 PG 87 PG 89

Training&Validation Performance on KGRA dataset.

lossaccuracy

If you find it useful, consider citing our paper once our paper is accepted or can be indexed:

Xu Q, Jia S, Jiang X, et al. Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection[J]. arXiv preprint arXiv:2209.03322, 2022. (Paper Submitted)

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Distinguishing between photographic images (PG) and computer-generated (CG) images is a difficult task. In this work, we construct an effective nerual network for this fundamental image forensic problem.

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