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

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

ECCV 2018poster108 citations

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

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

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.

BibTeX

@inproceedings{eccv2018_doublejpegdetect,
  title = {Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network},
  author = {Jinseok Park and Donghyeon Cho and Wonhyuk Ahn and Heung-Kyu Lee},
  booktitle = {ECCV 2018},
  year = {2018}
}