https://github.com/kitware/generated-image-detection
https://github.com/kitware/generated-image-detection
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/kitware/generated-image-detection
- Owner: Kitware
- License: other
- Created: 2021-10-20T20:19:52.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-06T17:34:04.000Z (almost 2 years ago)
- Last Synced: 2025-03-26T08:23:05.865Z (10 months ago)
- Language: Python
- Size: 465 KB
- Stars: 16
- Watchers: 3
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Kitware Generated Image Detector
## Overview
This repository contains software to detect computer-generated images, such as those created by Generative Adversarial Network (GAN) algorithms, distinguishing them from typical photos captured by cameras. A computer generated image is a strong indication that the content is fabricated, which is vital to ascertain for digital image forensics.
The [slides](doc/kitware_SG3_generated.pdf) provide an overview of the approach and the detection results on NVIDIA's [StyleGAN3 image generator](https://github.com/NVlabs/stylegan3). This algorithm was one of the detectors evaluated on StyleGAN3 images, see the details [here](https://github.com/NVlabs/stylegan3-detector#results). Overall, this detector was able to achieve a strong performance of **0.92** Area Under the ROC Curve (AUC) metric (1.0 being the perfect score) on this test data.
The model file (resnet101_v3.pt) is available at
https://drive.google.com/file/d/1iWjIj_2YEVCHDcE3uPw7c09hz7jRX5un/view?usp=sharing
Command line example:
python ./generated_image_detection/common/generated_image_classification.py --run_test --tile=224 --test_dir $pdata --model_arch resnet101 --batch_size 1 --use_gpu --test_model_name ./models/resnet101_v3.pt --mean 0.485,0.456,0.406 --std 0.229,0.224,0.225
For further information please contact Dr. Arslan Basharat ().
## License
This software is provided the uses a permissive [BSD License](LICENSE).
## Acknowledgements
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0123. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
Distribution A: Approved for public release: distribution unlimited.