Table of Contents

• Updates
• Central Idea
• Motivation
• Limitation of existing works
• Pipeline
• Instructions for Code usage
• Citation

CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search [CVPR 2023]

Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
MBZUAI, UAE.

Updates 📢

• July-19 : Code released.
• June-19 : Code and demo release coming soon. Stay tuned!

🎯 Central Idea 🎯

We all love sharing photos online, but do you know big companies and even governments can use sneaky 🕵️‍♂️ face recognition software to track us? Our research takes this challenge head-on with a simple and creative idea 🌟: using carefully crafted makeup 💄 to outsmart the tracking software. The cherry on top? We're using everyday, easy-to-understand language 🗣️ to guide the makeup application, giving users much more flexibility! Our approach keeps your photos safe 🛡️ from unwanted trackers without making you look weird or having bizarre patches on your face, issues commonly seen with previous solutions.

Motivation 💪 🔥

• Malicious black-box Face recognition systems pose a serious threat to personal security/privacy of 5 billions people using social media.
• Unauthorized entities can use FR systems to track user activities by scraping face images from social media platforms.
• There is an urgent demand for effective privacy preservation methods.

Limitation of existing works ⚠️

• Recent noise-based facial privacy protection approaches result in artefacts.
• Patch-based privacy approaches provide low privacy protection and their large visible pattern compromises naturalness.

Pipeline

CLIP2Protect generates face images that look natural and real. But here's the special part: it also ensures a high level of privacy protection. This means you can keep sharing images without worrying about unwanted tracking. It consists of two stages.

• The latent code initialization stage reconstructs the given face image in the latent space by fine-tuning the generative model.
• The text-guided adversarial optimization stage utilizes user-defined makeup text prompts and identity-preserving regularization to guide the search for adversarial codes within the latent space to effectively protect the facial privacy.

Intructions for Code usage

Setup

• Get code

git clone https://github.com/fahadshamshad/Clip2Protect.git

• Build environment

cd Clip2Protect
# use anaconda to build environment 
conda create -n clip2protect python=3.8
conda activate clip2protect
# install packages
pip install -r requirements.txt

Steps for Protecting Faces

1. Our solution relies on the Rosinality PyTorch implementation of StyleGAN2.

2. Download the pre-trained StyleGAN2 weights:

   • Download the pre-trained StyleGAN2 weights from here.
   • Place the weights in the 'pretrained_models' folder.

3. Download pretrained face recognition models and dataset instructions:

   • To acquire pretrained face recognition models and dataset instructions, including target images, please refer to the AMT-GAN page here.
   • Place the pretrained face recognition model in the models folder.

4. Acquire latent codes:

   • We assume the latent codes are available in the latents.pt file.
   • You can acquire the latent codes of the face images to be protected using the encoder4editing (e4e) method available here.

5. Run the code:

   • The core functionality is in main.py.
   • Provide the latents.pt file and the corresponding faces directory, named 'input_images'.
   • Generate the protected faces in the 'results' folder by running the following command:

     python main.py --data_dir input_images --latent_path latents.pt --protected_face_dir results

6. Generator finetuning and adversarial optimization stages:

   • The generator finetuning is implemented in pivot_tuning.py.
   • The adversarial optimization is implemented in adversarial_optimization.py.

Citation

If you're using CLIP2Protect in your research or applications, please cite using this BibTeX:

@inproceedings{shamshad2023clip2protect,
  title={CLIP2Protect: Protecting Facial Privacy Using Text-Guided Makeup via Adversarial Latent Search},
  author={Shamshad, Fahad and Naseer, Muzammal and Nandakumar, Karthik},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20595--20605},
  year={2023}
  }