Double JPEG Detection in Mixed JPEG Quality Factors Using Deep Convolutional Neural Network

Jinseok Park, Donghyeon Cho, Wonhyuk Ahn, Heung-Kyu Lee

ECCV 2018

doi:10.1007/978-3-030-01228-1_39 /eccv/2018/park2018eccv-double/

Abstract

Double JPEG detection is essential for detecting various image manipulations. This paper proposes a novel deep convolutional neural network for double JPEG detection using statistical histogram features from each block with a vectorized quantization table. In contrast to previous methods, the proposed approach handles mixed JPEG quality factors and is suitable for real-world situations. We collected real-world JPEG images from the image forensic service and generated a new double JPEG dataset with 1120 quantization tables to train the network. The proposed approach was verified experimentally to produce a state-of-the-art performance, successfully detecting various image manipulations.

PDF ECCV Semantic Scholar

Cite

Text

Park et al. "Double JPEG Detection in Mixed JPEG Quality Factors Using Deep Convolutional Neural Network." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01228-1_39

Markdown

[Park et al. "Double JPEG Detection in Mixed JPEG Quality Factors Using Deep Convolutional Neural Network." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/park2018eccv-double/) doi:10.1007/978-3-030-01228-1_39

BibTeX

@inproceedings{park2018eccv-double,
  title     = {{Double JPEG Detection in Mixed JPEG Quality Factors Using Deep Convolutional Neural Network}},
  author    = {Park, Jinseok and Cho, Donghyeon and Ahn, Wonhyuk and Lee, Heung-Kyu},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2018},
  doi       = {10.1007/978-3-030-01228-1_39},
  url       = {https://mlanthology.org/eccv/2018/park2018eccv-double/}
}