Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🔥🔥Defending Against Deepfakes Using Adversarial Attacks on Conditional Image Translation Networks
| Date | Stars |
|---|---|
| 2026-07-30 | 349 |
| 2026-09-24 | 350 |
| 2026-10-09 | 351 |
| 2026-11-04 | 351 |
Today
— stars today
This week
— stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.00%/day
## Disrupting Deepfakes: Adversarial Attacks on Conditional Image Translation Networks **[[Paper]](https://arxiv.org/abs/2003.01279)** **[[Demo Video]](https://youtu.be/7_7r4Ng4-bE)** <p align="center"><img width="100%" src="imgs/demo.gif" /></p> <p align="center"><img width="100%" src="imgs/main_1.gif" /></p> Official PyTorch implementation of **[Disrupting Deepfakes](https://arxiv.org/abs/2003.01279)** (to be presented at the **CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision**). This repository contains code for adversarial attacks (disruptions) for (conditional) image translation networks. The StarGAN, GANimation, pix2pixHD and CycleGAN networks are included - and the attacks can be adapted to any image translation network. We include adaptations of FGSM, I-FGSM and PGD attacks. We also include our class transferable attacks, adversarial training for image translation networks and spread-spectrum evasion of blur defenses. > **Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems**<br> > [Nataniel Ruiz](https://natanielruiz.github.io), [Sarah Adel Bargal](https://cs-people.bu.edu/sbargal/), [Stan Sclaroff](http://www.cs.bu.edu/~sclaroff/) <br/> > Boston University <br/> > https://arxiv.org/abs/2003.01279 <br> > > **Abstract:** *Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person's face, such systems can generate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes. In order to prevent a malicious user from generating modified images of a person without their consent we tackle the new problem of generating adversarial attacks against such image translation systems, which disrupt the resulting output image. We call this problem disrupting deepfakes. Most image translation architectures are generative models conditioned on an attribute (e.g. put a smile on this person's face). We are first to propose and successfully apply (1) class transferable adversarial attacks that generalize to different classes, which means that the attacker does not need to have knowledge about the conditioning class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks. Finally, in gray-box scenarios, blurring can mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blur defenses.* ## Datasets and Models **StarGAN Dataset** ``` cd stargan bash download.sh celeba ``` **StarGAN Models** ``` bash download.sh pretrained-celeba-256x256 ``` **GANimation Dataset** ``` cd ganimation mkdir -p data/celeba/images_aligned/new_small ``` We use a third-party public pre-processed (cropped faces) version of the CelebA dataset that can be found [here](https://drive.google.com/open?id=1MijMc6QnjrNFopT1G43WQFeei9ddcaza). Please download datasets/celebA/imgs.tar from this Google Drive folder, extract imgs.tar and move these images into ganimation/celeba/images_aligned/new_small **GANimation Models** The pretrained models can be downloaded [here](https://www.dropbox.com/sh/108g19dk3gt1l7l/AAB4OJHHrMHlBDbNK8aFQVZSa?dl=0). They should be located at ganimation/models **CycleGAN Dataset and Models** Follow instruction in the [CycleGAN official repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for downloading their models and data. **pix2pixHD Dataset and Models** Follow instruction in the [pix2pixHD official repository](https://github.com/NVIDIA/pix2pixHD) for downloading their models and data. ## Attack Testing Here are bash commands for testing our vanilla attacks on each different architecture. ``` # StarGAN Attack Test cd stargan python main.py --mode test --dataset CelebA --image_size 256 --c_
Excerpt of 6,654 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d6ce29726cbd24aa, topic:computer-vision, readme:computer vision
matched fp:d6ce29726cbd24aa, topic:deep-learning