Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs from pre-processed data, such as histogram features and/or decompressed images. In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input. Considering the DCT sub-band nature in JPEG, a multiple-branch CNN structure has been designed to reveal whether a JPEG format image has been doubly compressed. Comparing with previous methods, the proposed method provides end-to-end detection capability. Extensive experiments have been carried out to demonstrate the effectiveness of the proposed network.
@article{arxiv.1710.05477,
title = {A multi-branch convolutional neural network for detecting double JPEG compression},
author = {Bin Li and Hu Luo and Haoxin Zhang and Shunquan Tan and Zhongzhou Ji},
journal= {arXiv preprint arXiv:1710.05477},
year = {2017}
}
Comments
This paper was accepted by the 3rd International Workshop on Digital Crime and Forensics (IWDCF2017)