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Artificial Immune System of Secure Face Recognition Against Adversarial Attacks

This is the official code of IJCV paper 《Artificial Immune System of Secure Face Recognition Against Adversarial Attacks》

Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face recognition models. The primary difference between adversarial noise and general noise lies in its specificity. Adversarial attack methods give rise to noises tailored to the characteristics of the individual image and recognition model at hand. Diverse samples and recognition models can engender specific adversarial noise patterns, which pose significant challenges for adversarial defense. Addressing this challenge in the realm of face recognition presents a more formidable endeavor due to the inherent nature of face recognition as an open set task. In order to tackle this challenge, it is imperative to employ customized processing for each individual input sample. Drawing inspiration from the biological immune system, which can identify and respond to various threats, this paper aims to create an artificial immune system (AIS) to provide adversarial defense for face recognition. The proposed defense model incorporates the principles of antibody cloning, mutation, selection, and memory mechanisms to generate a distinct ``antibody" for each input sample, wherein the term "antibody" refers to a specialized noise removal manner. Furthermore, we introduce a self-supervised adversarial training mechanism that serves as a simulated rehearsal of immune system invasions. Extensive experimental results demonstrate the efficacy of the proposed method, surpassing state-of-the-art adversarial defense methods.

The proposed framework

Usage Instructions

Requirments

python == 3.7

pytorch == 1.6.0

torchvision == 0.7.0

Training

Data preparing

The proposed model is trained on CelebA, which comprises a substantial collection of 202,599 facial images.

train.py to train the defense model.

Facial image reconstruction

recon_face.py --read_path test.jpg --write_path recon.jpg to reconstruct facial image.

Pretrained model can be downloaded from Baidu Netdisk, code: dptb or Google Drive

Citation

If you find our work useful in your research, please consider to cite:

@ARTICLE{ren2024artificial,
  author={Ren, Min and Wang, Yunlong and Yuhao, Zhu and Yongzhen, Huang and Qi, Li and Sun, Zhenan and Tan, Tieniu},
  journal={International Journal of Computer Vision (IJCV)}, 
  title={Artificial Immune System of Secure Face Recognition Against Adversarial Attacks}, 
  year={2024},
  volume={},
  number={},
  pages={1-17},
  doi={https://doi.org/10.1007/s11263-024-02153-0}}