A multi-branch convolutional neural network for detecting double JPEG compression

Bin Li, Hu Luo, Haoxin Zhang, Shunquan Tan, Zhongzhou Ji Shenzhen Key Lab of Media Information Security, Shenzhen University, P. R. China ABSTRACT Detecting 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.