Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Related tags

Deep Learning SGADV
Overview

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Introduction

Run attack: SGADV.py

Objective function: foolbox/attacks/gradient_descent_base.py

New developed tools: foolbox/utils.py

Citation

If using this project in your research, please cite our paper.

Under review

Note

The code in the folder foolbox is derived from the project foolbox.

Images in the folder data are only examples from LFW and CelebA dataset.

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Comments
  • Some questions about the paper

    Some questions about the paper

    Since don't know how to get your email address, I am here to ask for advice. After reading your paper, I have the following questions I hope you can help me understand better：

    1. Is the ||·||∈[0,1] mentioned on page4 represent the dissimilarity score or just the feature space distance? It says lower indicates more similarity, but I see your code it's a function about cosine similarity, which the higher the more similarity. If this is the feature space distance, where use the dissimilarity score?
    2. the C-BCE objective function is useful, but why not use the Jsg function mentioned in formulation(13) to these label-based methods, You use this function to transform the PGD method to SGADV, can this optimization function be used in DeepFool, FGSM?
    3. I think SGADV is more like a PGD variation in FRS, and the convergence method is the reason why it outperforms the C-BCE objective function label-based method?
    opened by PPPPPPPeng 2
Owner
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