This paper studies video inpainting detection, which localizes an inpainted
region in a video both spatially and temporally. In particular, we introduce
VIDNet, Video Inpainting Detection Network, which contains a two-stream
encoder-decoder architecture with attention module. To reveal artifacts encoded
in compression, VIDNet additionally takes in Error Level Analysis frames to
augment RGB frames, producing multimodal features at different levels with an
encoder. Exploring spatial and temporal relationships, these features are
further decoded by a Convolutional LSTM to predict masks of inpainted regions.
In addition, when detecting whether a pixel is inpainted or not, we present a
quad-directional local attention module that borrows information from its
surrounding pixels from four directions. Extensive experiments are conducted to
validate our approach. We demonstrate, among other things, that VIDNet not only
outperforms by clear margins alternative inpainting detection methods but also
generalizes well on novel videos that are unseen during training.

该研究探讨了视频修补检测技术，提出了一种双流编码器解码器体系结构和注意力模块的 VIDNet 方法，该模型采用误差级别分析数据增强处理 RGB 帧，进一步利用卷积 LSTM 模型对编码得到的多模态特征进行空间和时间关系的探索，并使用四个方向的局部注意力模块来检测像素是否被修补，实验结果表明 VIDNet 方法能够在各方面超越其他同类方法并且具有很好的泛化能力。