Сочетание контрастного обучения и обучения с учителем для обнаружения видео со сверхвысоким разрешением

Препринты ИПМ им. М.В. Келдыша. 2022. № 80. С. 14–27.

Meshchaninov V., Ватолин Д. С., Молодецких И. А., Волобой А. Г.

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. In this work, we propose a new upscaled-resolutiondetection 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-theart alternatives. The code and models are publicly available at https://github.com/msu-video-group/SRDM

Keywords: компьютерное зрение, computer vision, Super-resolution, deepfake detection, повышение разрешения, обнаружение дипфейков