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

TL;DR

This paper introduces a neural network method that uses multiple DCT histograms and quantization table features to effectively detect double MPEG-4 compression, aiding in forgery identification.

Contribution

It presents a novel neural network approach leveraging multiple DCT histograms and quantization table features for improved double MPEG-4 compression detection.

Findings

High detection accuracy demonstrated in experiments

Effective exploitation of DCT domain artifacts

Enhanced performance with auxiliary quantization features

Abstract

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.