# Resampling detection of recompressed images via dual-stream convolutional neural network

arXiv: https://arxiv.org/abs/1901.04637 · HTML: https://mlsanity.com/paper/1901.04637/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

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

- [Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/2105.04888/) (2021)
- [Combining Contrastive and Supervised Learning for Video Super-Resolution Detection](https://mlsanity.com/paper/2205.10406/) (2022)

## Abstract (arXiv)

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.