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CNN Detection

in Software, Tools
2 min read

Detecting CNN-Generated Images

This repository contains models, evaluation code, and training code on datasets from our paper. If you would like to run our pretrained model on your image/dataset see (2) Quick start.
In this work we ask whether it is possible to create a “universal” detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN) is able to generalize surprisingly well to unseen architectures, datasets, and training methods (including the just released StyleGAN2). Our findings suggest the intriguing possibility that today’s CNN-generated images share some common systematic flaws, preventing them from achieving realistic image synthesis.

Install packages

  • Install PyTorch (pytorch.org)
  • pip install -r requirements.txt

Download model weights

  • Run bash weights/download_weights.sh

Quick start

Run on a single image

This command runs the model on a single image, and outputs the uncalibrated prediction.

LEARN MORE

Tags: DeepfakeDeepfake-toolsDeepfakelab
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