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

Jinseok Park, Donghyeon Cho, Wonhyuk Ahn, Heung-Kyu Lee; The European Conference on Computer Vision (ECCV), 2018, pp. 636-652

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Park_2018_ECCV,
author = {Park, Jinseok and Cho, Donghyeon and Ahn, Wonhyuk and Lee, Heung-Kyu},
title = {Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}