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GiantSeaweed / DECREE

Official repository for CVPR'23 paper: Detecting Backdoors in Pre-trained Encoders
https://arxiv.org/abs/2303.15180
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backdoor-attacks backdoor-detection contrastive-learning multi-modal self-supervised-learning

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DECREE

This is the code repository of the CVPR 2023 paper DECREE, "Detecting Backdoors in Pre-trained Encoders", the first backdoor detection method against self-supervising learning (SSL) backdoor attacks.

If you find our work and code useful in your research, please consider citing:

@InProceedings{Feng_2023_CVPR,
    author    = {Feng, Shiwei and Tao, Guanhong and Cheng, Siyuan and Shen, Guangyu and Xu, Xiangzhe and Liu, Yingqi and Zhang, Kaiyuan and Ma, Shiqing and Zhang, Xiangyu},
    title     = {Detecting Backdoors in Pre-Trained Encoders},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {16352-16362}
}

SSL Attacks

In this work, we focus on 3 types of SSL attacks on vision encoders:

Here is an illustration of backdoor attacks on SSL encoders:

Environment

Our testing environment: Python 3.8.5, torch 1.10.0, torchvision 0.11.1, numpy 1.18.5, pandas 1.1.5, pillow 7.2.0, and tqdm 4.64.0.

Download Encoders and Shadow Datasets

  1. Download encoders and shadow datasets from here and unzip it at the current path ./DECREE/.

  2. Unzip the imagenet.zip at ./DECREE/data/.

  3. Finally, the layout should look like below:

    DECREE
├── data
│   ├── cifar10
│   │   ├── test.npz
│   │   └── train.npz
│   └── imagenet
│       ├── ILSVRC2012_devkit_t12.tar.gz
│       ├── ...
│       └── val
├── output
│   ├── cifar10_resnet18
│   │   └── ...
│   └── CLIP_text
│       └── ...
├── README.md
├── main.py
├── ...
└── .gitignore

Validate Image-on-Image and Image-on-Pair Trojaned Encoders

We leverage the repo of BadEncoder[2].

Validate Text-on-Pair Trojaned Encoders

Since Carlini et al.[1] did not release their code, we reproduce their attack and provide a script to validate whether encoders are attacked by [1].

  1. We follow the description in [1] to reproduce their attack. Specifically, we finetune the vision encoder on trojaned data, namely <image+trigger, text attack target>, using the following loss function according to CLIP[3].

    Please refer to function train_text in file attack_encoder.py for more details.

    To reproduce the attack, run:

    python -u scripts/run_attack_encoder.py

  2. To validate whether encoders are attacked by Carlini et al.[1], run:

    python -u validate/script_compute_zscore.py

The z-score results will be shown in valid_cliptxt_zscore.txt. During experiments, encoders with z-score > 2.5 are considered as trojaned.

DECREE

To run the DECREE:

python run_decree.py

For the detection result, you can find:

(1) the inverted triggers in trigger_inv/,

(2) the optimization process in detect_log/, and

(3) the final L1-norm of the inverted triggers in trigger_norm/. The $\mathcal{PL}^1$-norm can be then easily computed from the L1-norm.

Acknowledgement

Our work and code are inspired by the following repositories:

  1. https://github.com/jinyuan-jia/BadEncoder
  2. https://github.com/openai/CLIP
  3. https://github.com/bolunwang/backdoor

Reference

  1. [ICLR'2022] Poisoning and Backdooring Contrastive Learning. Nicholas Carlini, Andreas Terzis. https://arxiv.org/abs/2106.09667
  2. [S&P'2022] BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learnin. Jinyuan Jia, Yupei Liu, Neil Zhenqiang Gong. https://arxiv.org/abs/2108.00352
  3. [ICML'2021] Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. https://arxiv.org/abs/2103.00020