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

2017/10/16 by Bin Li, Hu Luo, Li, Bin +7 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Processing Techniques and Applications #Multimedia (cs.MM)

paper · pdf · doi:10.48550/arxiv.1710.05477

openalex publication_date 2017/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Cited by

• DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

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