Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Overview

Adversarial Training Against Location-Optimized Adversarial Patches

arXiv | Paper | Code | Video | Slides

Code for the paper:

Sukrut Rao, David Stutz, Bernt Schiele. (2020) Adversarial Training Against Location-Optimized Adversarial Patches. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_32

Setup

Requirements

  • Python 3.7 or above
  • PyTorch
  • scipy
  • h5py
  • scikit-image
  • scikit-learn

Optional requirements

To use script to convert data to HDF5 format

  • torchvision
  • Pillow
  • pandas

To use Tensorboard logging

  • tensorboard

With the exception of Python and PyTorch, all requirements can be installed directly using pip:

$ pip install -r requirements.txt

Setting the paths

In common/paths.py, set the following variables:

  • BASE_DATA: base path for datasets.
  • BASE_EXPERIMENTS: base path for trained models and perturbations after attacks.
  • BASE_LOGS: base path for tensorboard logs (if used).

Data

Data needs to be provided in the HDF5 format. To use a dataset, use the following steps:

  • In common/paths.py, set BASE_DATA to the base path where data will be stored.
  • For each dataset, create a directory named <dataset-name> in BASE_DATA
  • Place the following files in this directory:
    • train_images.h5: Training images
    • train_labels.h5: Training labels
    • test_images.h5: Test images
    • test_labels.h5: Test labels

A script create_dataset_h5.py has been provided to convert data in a comma-separated CSV file consisting of full paths to images and their corresponding labels to a HDF5 file. To use this script, first set BASE_DATA in common/paths.py. If the files containing training and test data paths and labels are train.csv and test.csv respectively, use:

$ python scripts/create_dataset_h5.py --train_csv /path/to/train.csv --test_csv /path/to/test.csv --dataset dataset_name

where dataset_name is the name for the dataset.

Training and evaluating a model

Training

To train a model, use:

$ python scripts/train.py [options]

A list of available options and their descriptions can be found by using:

$ python scripts/train.py -h

Evaluation

To evaluate a trained model, use:

$ python scripts/evaluate.py [options]

A list of available options and their descriptions can be found by using:

$ python scripts/evaluate.py -h

Using models and attacks from the paper

The following provides the arguments to use with the training and evaluation scripts to train the models and run the attacks described in the paper. The commands below assume that the dataset is named cifar10 and has 10 classes.

Models

Normal

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --cuda --mode normal --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

Occlusion

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 1 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

AT-Fixed

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_pos 3 3 --mask_dims 8 8 --mode adversarial --location fixed --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

AT-Rand

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

AT-RandLO

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --optimize_location --opt_type random --stride 2 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

AT-FullLO

$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --optimize_location --opt_type full --stride 2 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip

Attacks

The arguments used here correspond to using 100 iterations and 30 attempts. These can be changed by appropriately setting --iterations and --attempts respectively.

AP-Fixed

$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_pos 3 3 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location fixed --epsilon 0.05 --iterations 100 --signed_grad --perturbations_file perturbations --use_tensorboard

AP-Rand

$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --signed_grad --perturbations_file perturbations --use_tensorboard

AP-RandLO

$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --optimize_location --opt_type random --stride 2 --signed_grad --perturbations_file perturbations --use_tensorboard

AP-FullLO

$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --optimize_location --opt_type full --stride 2 --signed_grad --perturbations_file perturbations --use_tensorboard

Citation

Please cite the paper as follows:

@InProceedings{Rao2020Adversarial,
author = {Sukrut Rao and David Stutz and Bernt Schiele},
title = {Adversarial Training Against Location-Optimized Adversarial Patches},
booktitle = {Computer Vision -- ECCV 2020 Workshops},
year = {2020},
editor = {Adrien Bartoli and Andrea Fusiello},
publisher = {Springer International Publishing},
address = {Cham},
pages = {429--448},
isbn = {978-3-030-68238-5}
}

Acknowledgement

This repository uses code from davidstutz/confidence-calibrated-adversarial-training.

License

Copyright (c) 2020 Sukrut Rao, David Stutz, Max-Planck-Gesellschaft

Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You understand and agree that the authors are under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. The authors nevertheless reserve the right to update, modify, or discontinue the Software at any time.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. You agree to cite the corresponding papers (see above) in documents and papers that report on research using the Software.

Comments
  • Towards Better Understanding Attribution Methods

    Towards Better Understanding Attribution Methods

    The code for Towards Better Understanding Attribution Methods(https://github.com/sukrutrao/Attribution-Evaluation) seems to be missing. Where can I find it?

    opened by yuejinghang 1
  • Bump pillow from 7.2.0 to 9.0.1

    Bump pillow from 7.2.0 to 9.0.1

    Bumps pillow from 7.2.0 to 9.0.1.

    Release notes

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    9.0.1

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html

    Changes

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@​radarhere, @​hugovk]
    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

    9.0.0

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    Changelog

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    9.0.1 (2022-02-03)

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [radarhere, hugovk]

    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

    9.0.0 (2022-01-02)

    • Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

    • Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

    • Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

    • Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

    • Improved I;16 operations on big endian #5901 [radarhere]

    • Limit quantized palette to number of colors #5879 [radarhere]

    • Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

    • When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

    • Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

    • Added rounding when converting P and PA #5824 [radarhere]

    • Improved putdata() documentation and data handling #5910 [radarhere]

    • Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

    • Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

    ... (truncated)

    Commits
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    • 02affaa Added delay after opening image with xdg-open
    • ca0b585 Updated formatting
    • 427221e In show_file, use os.remove to remove temporary images
    • c930be0 Restrict builtins within lambdas for ImageMath.eval
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  • Bump pillow from 7.2.0 to 9.0.0

    Bump pillow from 7.2.0 to 9.0.0

    Bumps pillow from 7.2.0 to 9.0.0.

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    9.0.0 (2022-01-02)

    • Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

    • Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

    • Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

    • Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

    • Improved I;16 operations on big endian #5901 [radarhere]

    • Limit quantized palette to number of colors #5879 [radarhere]

    • Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

    • When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

    • Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

    • Added rounding when converting P and PA #5824 [radarhere]

    • Improved putdata() documentation and data handling #5910 [radarhere]

    • Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

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  • Bump pillow from 7.2.0 to 8.3.2

    Bump pillow from 7.2.0 to 8.3.2

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    Security

    • CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

    • Fix 6-byte OOB read in FliDecode [wiredfool]

    Python 3.10 wheels

    • Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

    Fixed regressions

    • Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

    • Updates for ImagePalette channel order #5599 [radarhere]

    • Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

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    • CVE-2021-23437 Raise ValueError if color specifier is too long [hugovk, radarhere]

    • Fix 6-byte OOB read in FliDecode [wiredfool]

    • Add support for Python 3.10 #5569, #5570 [hugovk, radarhere]

    • Ensure TIFF RowsPerStrip is multiple of 8 for JPEG compression #5588 [kmilos, radarhere]

    • Updates for ImagePalette channel order #5599 [radarhere]

    • Hide FriBiDi shim symbols to avoid conflict with real FriBiDi library #5651 [nulano]

    8.3.1 (2021-07-06)

    • Catch OSError when checking if fp is sys.stdout #5585 [radarhere]

    • Handle removing orientation from alternate types of EXIF data #5584 [radarhere]

    • Make Image.array take optional dtype argument #5572 [t-vi, radarhere]

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    • Allow ICNS save on all operating systems #4526 [baletu, radarhere, newpanjing, hugovk]

    • De-zigzag JPEG's DQT when loading; deprecate convert_dict_qtables #4989 [gofr, radarhere]

    • Replaced xml.etree.ElementTree #5565 [radarhere]

    ... (truncated)

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  • Bump pillow from 7.2.0 to 8.2.0

    Bump pillow from 7.2.0 to 8.2.0

    Bumps pillow from 7.2.0 to 8.2.0.

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    8.2.0

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    ... (truncated)

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    8.2.0 (2021-04-01)

    • Added getxmp() method #5144 [UrielMaD, radarhere]

    • Add ImageShow support for GraphicsMagick #5349 [latosha-maltba, radarhere]

    • Do not load transparent pixels from subsequent GIF frames #5333 [zewt, radarhere]

    • Use LZW encoding when saving GIF images #5291 [raygard]

    • Set all transparent colors to be equal in quantize() #5282 [radarhere]

    • Allow PixelAccess to use Python int when parsing x and y #5206 [radarhere]

    • Removed Image._MODEINFO #5316 [radarhere]

    • Add preserve_tone option to autocontrast #5350 [elejke, radarhere]

    • Fixed linear_gradient and radial_gradient I and F modes #5274 [radarhere]

    • Add support for reading TIFFs with PlanarConfiguration=2 #5364 [kkopachev, wiredfool, nulano]

    • Deprecated categories #5351 [radarhere]

    • Do not premultiply alpha when resizing with Image.NEAREST resampling #5304 [nulano]

    • Dynamically link FriBiDi instead of Raqm #5062 [nulano]

    • Allow fewer PNG palette entries than the bit depth maximum when saving #5330 [radarhere]

    • Use duration from info dictionary when saving WebP #5338 [radarhere]

    • Stop flattening EXIF IFD into getexif() #4947 [radarhere, kkopachev]

    ... (truncated)

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    • 496245a Fix BLP DOS -- CVE-2021-28678
    • 22e9bee Fix DOS in PSDImagePlugin -- CVE-2021-28675
    • ba65f0b Fix Memory DOS in ImageFont
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  • Bump pillow from 7.2.0 to 8.1.1

    Bump pillow from 7.2.0 to 8.1.1

    Bumps pillow from 7.2.0 to 8.1.1.

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    8.1.1

    https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html

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    8.1.1 (2021-03-01)

    • Use more specific regex chars to prevent ReDoS. CVE-2021-25292 [hugovk]

    • Fix OOB Read in TiffDecode.c, and check the tile validity before reading. CVE-2021-25291 [wiredfool]

    • Fix negative size read in TiffDecode.c. CVE-2021-25290 [wiredfool]

    • Fix OOB read in SgiRleDecode.c. CVE-2021-25293 [wiredfool]

    • Incorrect error code checking in TiffDecode.c. CVE-2021-25289 [wiredfool]

    • PyModule_AddObject fix for Python 3.10 #5194 [radarhere]

    8.1.0 (2021-01-02)

    • Fix TIFF OOB Write error. CVE-2020-35654 #5175 [wiredfool]

    • Fix for Read Overflow in PCX Decoding. CVE-2020-35653 #5174 [wiredfool, radarhere]

    • Fix for SGI Decode buffer overrun. CVE-2020-35655 #5173 [wiredfool, radarhere]

    • Fix OOB Read when saving GIF of xsize=1 #5149 [wiredfool]

    • Makefile updates #5159 [wiredfool, radarhere]

    • Add support for PySide6 #5161 [hugovk]

    • Use disposal settings from previous frame in APNG #5126 [radarhere]

    • Added exception explaining that repr_png saves to PNG #5139 [radarhere]

    • Use previous disposal method in GIF load_end #5125 [radarhere]

    ... (truncated)

    Commits
    • 741d874 8.1.1 version bump
    • 179cd1c Added 8.1.1 release notes to index
    • 7d29665 Update CHANGES.rst [ci skip]
    • d25036f Credits
    • 973a4c3 Release notes for 8.1.1
    • 521dab9 Use more specific regex chars to prevent ReDoS
    • 8b8076b Fix for CVE-2021-25291
    • e25be1e Fix negative size read in TiffDecode.c
    • f891baa Fix OOB read in SgiRleDecode.c
    • cbfdde7 Incorrect error code checking in TiffDecode.c
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump pillow from 7.2.0 to 9.3.0

    Bump pillow from 7.2.0 to 9.3.0

    Bumps pillow from 7.2.0 to 9.3.0.

    Release notes

    Sourced from pillow's releases.

    9.3.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

    Changes

    ... (truncated)

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    Sourced from pillow's changelog.

    9.3.0 (2022-10-29)

    • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

    • Initialize libtiff buffer when saving #6699 [radarhere]

    • Inline fname2char to fix memory leak #6329 [nulano]

    • Fix memory leaks related to text features #6330 [nulano]

    • Use double quotes for version check on old CPython on Windows #6695 [hugovk]

    • Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

    • Fixed set_variation_by_name offset #6445 [radarhere]

    • Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

    • Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

    • Added ExifTags enums #6630 [radarhere]

    • Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

    • Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

    • Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

    • Added GPS TIFF tag info #6661 [radarhere]

    • Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

    • Do not attempt normalization if mode is already normal #6644 [radarhere]

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • some error

    some error

    hello, i run your code, i find some error. can you help me ?

    File "scripts/train.py", line 90, in run mask_gen = MaskGenerator(img_dims, tuple(args.mask_dims), TypeError: 'NoneType' object is not iterable

    when i run the normal model training step on GTSRB dataset ,i get this error. this code will generate a mask? but the normal model don't need the mask of adversarial?

    opened by huangxingseng 0
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