  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.
