Combining contrastive and supervised learning for video super-resolution detection
Abstract:
Upscaled video detection is a helpful tool in multimedia forensics, but it’s 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. This paper proposes 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, the major components of our framework are systematically reviewed — in particular, it is shown that most data-augmentation approaches hinder the learning of the method. Through extensive experiments on various datasets, our method has been shown to effectively detects upscaling even in compressed videos and outperforms the state-of-theart alternatives. The code and models are publicly available at https://github.com/msu-video-group/SRDM.
Keywords:
native resolution, upscaling detection, super-resolution, interpolation, video compression
