  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
