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

arXiv: https://arxiv.org/abs/1710.05477 · HTML: https://mlsanity.com/paper/1710.05477/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network](https://mlsanity.com/paper/2104.04765/) (2021)

## Abstract (arXiv)

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.