Double compression is accompanied by various types of video manipulation and its traces can be exploited to determine whether a video is a forgery. This Letter presents a convolutional neural network for detecting double compression in MPEG-4 videos. Through analysis of the intra-coding process, we utilize two refined features for capturing the subtle artifacts caused by double compression. The discrete cosine transform (DCT) histogram feature effectively detects the change of statistical characteristics in DCT coefficients and the parameter-based feature is utilized as auxiliary information to help the network learn double compression artifacts. When compared with state-of-the-art networks and forensic method, the results show that the proposed approach achieves a higher performance.

中文翻译：

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使用基于细化特征的 CNN 检测 MPEG-4 视频中的双重压缩

双重压缩伴随着各种类型的视频操作，其痕迹可用于确定视频是否是伪造的。本文介绍了一种卷积神经网络，用于检测 MPEG-4 视频中的双重压缩。通过对帧内编码过程的分析，我们利用两个改进的特征来捕获由双重压缩引起的细微伪影。离散余弦变换 (DCT) 直方图特征有效检测 DCT 系数中统计特征的变化，并利用基于参数的特征作为辅助信息，帮助网络学习双压缩伪影。与最先进的网络和取证方法相比，结果表明所提出的方法实现了更高的性能。