Deep video inpainting is typically used as malicious manipulation to remove
important objects for creating fake videos. It is significant to identify the
inpainted regions blindly. This letter proposes a simple yet effective forensic
scheme for Video Inpainting LOcalization with ContrAstive Learning (ViLocal).
Specifically, a 3D Uniformer encoder is applied to the video noise residual for
learning effective spatiotemporal forensic features. To enhance the
discriminative power, supervised contrastive learning is adopted to capture the
local inconsistency of inpainted videos through attracting/repelling the
positive/negative pristine and forged pixel pairs. A pixel-wise inpainting
localization map is yielded by a lightweight convolution decoder with a
specialized two-stage training strategy. To prepare enough training samples, we
build a video object segmentation dataset of 2500 videos with pixel-level
annotations per frame. Extensive experimental results validate the superiority
of ViLocal over state-of-the-arts. Code and dataset will be available at
this https URL

基于对比学习的 Video Inpainting LOcalization (ViLocal) 方案使用 3D Uniformer 编码器学习了有效的时空取证特征，通过吸引 / 排斥正 / 负原始和伪造像素对捕捉到了图像修复不一致，然后通过专门的两阶段训练策略利用轻量级卷积解码器生成了一个像素级修复定位地图，实验证明 ViLocal 优于现有技术。