# generated-image-detection

**Repository Path**: mirrors_Kitware/generated-image-detection

## Basic Information

- **Project Name**: generated-image-detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2026-01-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 (<arslan.basharat@kitware.com>).

## 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.