# DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms

arXiv: https://arxiv.org/abs/2107.08939 · HTML: https://mlsanity.com/paper/2107.08939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1708.00930/) (2017)
- [Deep Convolutional Neural Network for Identifying Seam-Carving Forgery](https://mlsanity.com/paper/2007.02393/) (2020)
- [Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features](https://mlsanity.com/paper/2103.13674/) (2021)

## Abstract (arXiv)

In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices. Double compression is accompanied by various types of video manipulation, and its traces can be exploited to determine whether a video is a forgery. To this end, we present a neural network-based approach with discriminant features for capturing peculiar artifacts in the discrete cosine transform (DCT) domain caused by double MPEG-4 compression. By analyzing the intra-coding process of MPEG-4, which performs block-DCT-based quantization, we exploit multiple DCT histograms as features to focus on the statistical properties of DCT coefficients on multiresolution blocks. Furthermore, we improve detection performance using a vectorized feature of the quantization table on dense layers as auxiliary information. Compared with neural network-based approaches suitable for exploring subtle manipulations, the experimental results reveal that this work achieves high performance.