https://github.com/cdluminate/robdml
Enhancing Adversarial Robustness for Deep Metric Learning, CVPR, 2022
https://github.com/cdluminate/robdml
adversarial-defense cvpr2022 deep-metric-learning
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Enhancing Adversarial Robustness for Deep Metric Learning, CVPR, 2022
- Host: GitHub
- URL: https://github.com/cdluminate/robdml
- Owner: cdluminate
- License: apache-2.0
- Created: 2021-10-28T19:05:38.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-02T16:49:06.000Z (over 2 years ago)
- Last Synced: 2025-03-18T20:13:15.619Z (2 months ago)
- Topics: adversarial-defense, cvpr2022, deep-metric-learning
- Language: Python
- Homepage: https://arxiv.org/abs/2203.01439
- Size: 15.4 MB
- Stars: 20
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Robust Deep Metric Learning (RobDML)
===
**Paper:** Mo Zhou, Vishal M. Patel, *Enhancing Adversarial Robustness for Deep Metric Learning*, CVPR, 2022
**PDF:** 
[[OpenAccess]](https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.html)
**Code:** https://github.com/cdluminate/robrank
**Poster:** [[PDF Version]](slides/poster.pdf) [[SVG Source]](slides/poster.svg) [[PNG Version]](slides/poster.png)
**Slides:** [[Full-length Presentation Version]](slides/slides.pdf) [[Single-page Version]](slides/slides-singlepage.pdf)
**Video:** [[Bilibili (English)]](https://www.bilibili.com/video/BV1Y34y1j7g4) [[YouTube (English)]](https://www.youtube.com/watch?v=IQLvG0wjB-I) [[Bilibili (Chinese)]](https://www.bilibili.com/video/BV1GY4y1z7No/) [[YouTube (Chinese)]](https://www.youtube.com/watch?v=ATqDBZIpZwQ)
## "Brief" code usage instructions:
Our code is based on the public code of the following paper:
["Adversarial Attack and Defense in Deep Ranking" arXiv: 2106.03614](https://github.com/cdluminate/robrank).
1. download and prepare datasets as described in [`README.md`](https://github.com/cdluminate/robrank/blob/main/README.md).
2. generate python configurations for the models by running `bash tools/autogen.bash`.
3. train our defensive models on CUB, CARS, SOP datasets:
```
=== Command Table ===
---------------------------------------------------------------------------------
DATASET | METHOD | COMMAND
---------------------------------------------------------------------------------
CUB | HM[S,g_LGA] | python3 bin/train.py -C cub:rres18ghmetsm:ptripletN
CARS | HM[S,g_LGA] | python3 bin/train.py -C cars:rres18ghmetsm:ptripletN
SOP | HM[S,g_LGA] | python3 bin/train.py -C sop:rres18ghmetsm:ptripletN
---------------------------------------------------------------------------------
CUB | HM[S,g_LGA]&ICS | python3 bin/train.py -C cub:rres18ghmetsmi:ptripletN
CARS | HM[S,g_LGA]&ICS | python3 bin/train.py -C cars:rres18ghmetsmi:ptripletN
SOP | HM[S,g_LGA]&ICS | python3 bin/train.py -C sop:rres18ghmetsmi:ptripletN
---------------------------------------------------------------------------------
=== Model Config Grammar ===
To simplify programming with various possible model combinations, the model
configs are passed altogether to the -C parameter for bin/train.py, with
colon being the separator. It follows the following grammar:
::
Please read https://github.com/cdluminate/robrank/blob/main/README.md first
for the details. Here we shall cover the additional models in the CVPR2022
paper. Take ResNet-18 as an example. The backbone is named `rres18` for deep
metric learning. Then by adding different suffixes, we will use different
defense methods in the paper. It follows the following grammar:
where:
backbone e.g. rres18
if_gradual_adversary in {g,} for (yes, no)
if_fix_anchor in {rh,hm} for (yes, no)
distance in {kl,l2,et} for (kl-divergence,euclidean-distance,thresholded-euclidean)
source_hardness or destination_hardness in {r,m,s,d,h}
if_intra_structure in {i,} for (yes, no)
For example, if we want the ResNet-18 (rres18) with gradual adversary (g), no
fixed anchor (hm), thresholded-euclidean distance (et), softhard source hardness (s),
semihard destination hardness (m), with intra-class loss (i), we combine the corresponding
abbreviations into (rres18ghmetsmi). Please refer the following lookup table.
---------------------------------------------------------------------------------
NAME | ABBR | EXAMPLE
---------------------------------------------------------------------------------
Random triplet sampling | R,r | rres18ghmet(r)m
Semihard triplet sampling | M,m | rres18ghmetr(m)
Softhard triplet sampling | S,s | rres18ghmet(s)m
Distance triplet sampling | D,d | rres18ghmet(d)m
Hardest triplet sampling | H,h | rres18ghmet(h)m
---------------------------------------------------------------------------------
You may read robrank/models/autogen/autogen.py for more detail on the combinations.
Unfortunately such flexibility resulted in 1800 possible model configurations,
where most of them are not working and not used in by the paper.
Meh... I understand that the code can be improved to simplify this, because there
are too many unused leftover code from early exploration of this work.
```
4. evaluate these models following the description in [`README.md`](https://github.com/cdluminate/robrank/blob/main/README.md)
## Bibtex
```bib
@InProceedings{robdml,
author = {Zhou, Mo and Patel, Vishal M.},
title = {Enhancing Adversarial Robustness for Deep Metric Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {15325-15334}
}
```
or if you prefer the arxiv version:
```bib
@article{robdml-arxiv,
author={Zhou, Mo and Patel, Vishal M.},
title={Enhancing Adversarial Robustness for Deep Metric Learning},
journal={arXiv preprint arXiv:2203.01439},
year={2022}
}
```
## License
Apache-2.0