DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms
This addresses video forensics for surveillance and devices, but it is incremental as it builds on existing neural network methods for compression detection.
The paper tackles detecting double MPEG-4 compression in videos to identify forgeries by proposing a neural network that uses multiple DCT histograms and quantization table features, achieving high performance in experiments.
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.