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SRDM — Super-Resolution Detection Method

SRDM is a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses.

See our paper for details.

Dependencies and Installation

Installation

  1. Clone repo

    git clone https://github.com/msu-video-group/SRDM.git
cd SRDM

  2. Install dependent packages

     pip install -r requirements.txt

Quick Inference

  1. Download pre-trained models from Google Drive. Place the models in ./pretrained_models. We provide two models: MobileNet and ResNet.
  2. Place your own videos as:
  $ tree data
  ├── data
      ├── video_001
      │   ├── frame_001.png
      │   ├── frame_002.png
      │   └── ...
      ├── video_002
      │   ├── frame_001.png
      │   ├── frame_002.png
      │   └── ...
      └── ...
  1. We provide a criterion for fake-resolution video. The method returns the probability of fake resolution for each frame. Then if 0.1 quantile of probabilities is bigger than 0.5 the video will be considered as fake-resolution video. You can config your own criteron in the test.py
  2. Run test. 
    python3 test.py --pretrained=<checkpoint> --test-data-path=<data path> --n_frames=<> --embedding-size=<> --model=<>
  3. For example. 
    python3 test.py --pretrained=./pretrained_models/resnet-emb-64-n_fr-3.pt --test-data-path=<data path> --n_frames=3 \
        --embedding-size=64 --model=resnet

Description

SRDM is initially described in paper. It's a novel approach to video-super-resolution detection that combines contrasting and supervised learning. The method first uses a ResNet backbone to extract deep features and then uses a lightweight MLP networks to project and classify representation of input frames. Our method achieved good performance in extensive experiments.

teaser

Main Results on MSU Video Super-Resolution Benchmark

SR method Accuracy SR method Accuracy
Real-ESRGAN 0.875 Real-ESRnet 1.000
DynaVSR-R 1.000 DynaVSR-V 1.000
D3Dnet 1.000 DBVSR 1.000
DUF-16L 1.000 DUF-28L 1.000
ESPCN 0.875 LGFN 1.000
RRN-10L 1.000 RRN-5L 1.000
SOF-VSR-BD 1.000 SOF-VSR-BI 1.000
TDAN 1.000 RealSR 0.875
ESRGAN 0.625 TGA 1.000
RSDN 1.000 RBPN 1.000
iSeeBetter 1.000 TMNet 1.000
GFPGAN 1.000 SwinIR 0.875
SRMD 1.000 comisr 1.000
waifu2x-cunet 1.000 waifu2x-anime 1.000
BasicSR 1.000 VRT 1.000

Training

Data preparation

  $ tree data
  ├── train
  │   ├── original
  │   │   ├── GT
  │   │   │   ├── video_001
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   ├── video_002
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   └── ...
  │   │   ├── SR method 1
  │   │   │   ├── video_001
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   ├── video_002
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   └── ...
  │   │   └── ...
  │   └── compressed
  │   │   │   ├── video_001
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   ├── video_002
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   └── ...
  │   │   ├── SR method 1
  │   │   │   ├── video_001
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   ├── video_002
  │   │   │   │   ├── frame_001.png
  │   │   │   │   └── ...
  │   │   │   └── ...
  │   │   └── ...
  └── val
      └── ...

Training from scratch

To train a SRDM on your data from scratch, change path to your dataset and run:

python3 main_worker.py --num_workers=2 --n_frames=2 --model-name=resnet --batch-size=32

Cite Us

@article{meshchaninov2022combining,
  title={Combining Contrastive and Supervised Learning for Video Super-Resolution Detection},
  author={Meshchaninov, Viacheslav and Molodetskikh, Ivan and Vatolin, Dmitriy},
  journal={arXiv preprint arXiv:2205.10406},
  year={2022}
}