论文标题
结合对比度和监督的学习视频超分辨率检测
Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
论文作者
论文摘要
放大视频检测是多媒体取证中的有用工具，但这是一项艰巨的任务，涉及各种升级和压缩算法。有许多分辨率增强方法，包括插值和基于深度学习的超分辨率，它们留下了独特的痕迹。在这项工作中，我们提出了一种基于使用对比和跨透镜损失的视觉表示方法的新的高尺度分辨率检测方法。为了解释该方法如何检测视频，我们系统地回顾了框架的主要组成部分 - 特别是，我们表明大多数数据提升方法都阻碍了该方法的学习。通过在各种数据集上进行的大量实验，我们证明我们的方法即使在压缩视频中也可以有效地检测到升级，并且表现优于最先进的替代方案。代码和模型可在https://github.com/msu-video-group/srdm上公开获得
Upscaled video detection is a helpful tool in multimedia forensics, but it is a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning-based super-resolution, and they leave unique traces. In this work, we propose a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. To explain how the method detects videos, we systematically review the major components of our framework - in particular, we show that most data-augmentation approaches hinder the learning of the method. Through extensive experiments on various datasets, we demonstrate that our method effectively detects upscaling even in compressed videos and outperforms the state-of-the-art alternatives. The code and models are publicly available at https://github.com/msu-video-group/SRDM
