Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Overview

Beyond the Spectrum

Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis" by Yang He, Ning Yu, Margret Keuper and Mario Fritz.

Pretrained Models

We release the model trained on CelebA-HQ dataset with image resolution 1024x1024. For the super resolution, we use 25,000 real images from CelebA-HQ to train it. For the detectors, we use 25,000 real images and 25,000 fake images to train a binary classifier based on ResNet-50.

We release some models as examples to show how to apply our models based on pixel-level or stage5-level reconstruction errors to detect deepfakes. Download link: https://drive.google.com/file/d/1FeIgABjBpjtnXT-Hl6p5a5lpZxINzXwv/view?usp=sharing.

If you have further questions regarding the trained models, please feel free to contact.

Train

  1. Train the super resolution model.

We use Residual Dense Network (RDN) in our work. The following script shows the hyperparameters used in our experiments. To be noticed, we only use 4 images to show how to run the script. For simplicity, you can download the pretrained model from the above link.

bash script/train_super_resolution_celeba.sh [GPU_ID]
  1. Train the detectors.

After obtaining the super resolution, we use pixel-level or stage5-level L1 based recontruction error to train a classifier. The following scripts use 10 training example to show how to train a classifier with a given super resolution model. You may need to adjust the learning rate and number of training epochs in your case.

bash script/train_pixel_pggan.sh [GPU_ID]
  1. Finetune with auxiliary tasks

In order to improve the robustness of our detectors, we introduce auxiliary tasks (i.e., colorization or denoising) into the super resolution model training and finetune the whole model end-to-end. The following scripts show how to train a model with those tasks.

bash script/train_pixel_pggan_colorization.sh [GPU_ID]
bash script/train_stage5_stylegan_denoising.sh [GPU_ID]

Test

Please download our models. You can use pixel-level or stage5-level to perform deepfakes detection.

bash script/test_pixel_celeba.sh [GPU_ID]
bash script/test_stage5_celeba.sh [GPU_ID]

Citation

If our work is useful for you, please cite our paper:

@inproceedings{yang_ijcai21,
  title={Beyond the Spectrum: Detecting Deepfakes via Re-synthesis},
  author={Yang He and Ning Yu and Margret Keuper and Mario Fritz},
  booktitle={30th International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2021}
}

Contact: Yang He ([email protected])

Last update: 08-22-2021

Owner
Yang He
Applied Scientist in Amazon Last Mile PostDoc in CISPA Helmholtz Center for Information Security / PhD in Max Planck Institute for Informatics
Yang He
A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

Sense-GVT 14 Jul 07, 2022
🇰🇷 Text to Image in Korean

KoDALLE Utilizing pretrained language model’s token embedding layer and position embedding layer as DALLE’s text encoder. Background Training DALLE mo

HappyFace 74 Sep 22, 2022
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022
Data manipulation and transformation for audio signal processing, powered by PyTorch

torchaudio: an audio library for PyTorch The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the

1.9k Dec 28, 2022
Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

CoTuning Official implementation for NeurIPS 2020 paper Co-Tuning for Transfer Learning. [News] 2021/01/13 The COCO 70 dataset used in the paper is av

THUML @ Tsinghua University 35 Sep 23, 2022
This is a repository of our model for weakly-supervised video dense anticipation.

Introduction This is a repository of our model for weakly-supervised video dense anticipation. More results on GTEA, Epic-Kitchens etc. will come soon

2 Apr 09, 2022
Training BERT with Compute/Time (Academic) Budget

Training BERT with Compute/Time (Academic) Budget This repository contains scripts for pre-training and finetuning BERT-like models with limited time

Intel Labs 263 Jan 07, 2023
Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

News! Aug 2020: v0.4.0 version of AlphaPose is released! Stronger tracking! Include whole body(face,hand,foot) keypoints! Colab now available. Dec 201

Machine Vision and Intelligence Group @ SJTU 6.7k Dec 28, 2022
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas FĂĽrst* 1, Elisabeth Rumetshofer* 1, Viet Tran1, Hubert Ramsauer1, Fei Tang3, Joh

Institute for Machine Learning, Johannes Kepler University Linz 133 Jan 04, 2023
Oriented Object Detection: Oriented RepPoints + Swin Transformer/ReResNet

Oriented RepPoints for Aerial Object Detection The code for the implementation of “Oriented RepPoints + Swin Transformer/ReResNet”. Introduction Based

96 Dec 13, 2022
Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers.

Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers. It contains purchases, recurring

Ayodeji Yekeen 1 Jan 01, 2022
End-to-end image segmentation kit based on PaddlePaddle.

English | 简体中文 PaddleSeg PaddleSeg has released the new version including the following features: Our team won the 6.2k Jan 02, 2023

Beginner-friendly repository for Hacktober Fest 2021. Start your contribution to open source through baby steps. đź’ś

Hacktober Fest 2021 🎉 Open source is changing the world – one contribution at a time! 🎉 This repository is made for beginners who are unfamiliar wit

Abhilash M Nair 32 Dec 11, 2022
Out of Distribution Detection on Natural Adversarial Examples

OOD-on-NAE Research project on out of distribution detection for the Computer Vision course by Prof. Rob Fergus (CSCI-GA 2271) Paper out on arXiv - ht

Anugya 1 Jun 08, 2022
Official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

1 SNAS4MTF This repo is the official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting. 1.1 The frame

SZJ 5 Sep 21, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
Image Captioning using CNN ,LSTM and Attention

Image Captioning using CNN ,LSTM and Attention This is a deeplearning model which tries to summarize an image into a text . Installation Install this

ASUTOSH GHANTO 1 Dec 16, 2021
Learning Visual Words for Weakly-Supervised Semantic Segmentation

[IJCAI 2021] Learning Visual Words for Weakly-Supervised Semantic Segmentation Implementation of IJCAI 2021 paper Learning Visual Words for Weakly-Sup

Lixiang Ru 24 Oct 05, 2022
Weakly Supervised 3D Object Detection from Point Cloud with Only Image Level Annotation

SCCKTIM Weakly Supervised 3D Object Detection from Point Cloud with Only Image-Level Annotation Our code will be available soon. The class knowledge t

1 Nov 12, 2021
Dynamic Graph Event Detection

DyGED Dynamic Graph Event Detection Get Started pip install -r requirements.txt TODO Paper link to arxiv, and how to cite. Twitter Weather dataset tra

Mert KoĹźan 3 May 09, 2022