Double JPEG Detection in Mixed JPEG Quality
Factors using Deep Convolutional Neural
Network
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-theart performance, successfully detecting various image manipulations.