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README.md
bitdepth.ipynb
cat.jpg
crop.ipynb
defense.py
imagenet_labels.py
inceptionv3.py
jpeg.ipynb
quilt.ipynb
quilt_preprocess.py
robustml_attack.py
robustml_model.py
setup.sh
tv.ipynb
utils.py

README.md

Countering Adversarial Images using Input Transformations

Paper: Guo et al. 2018

Setup

Run ./setup.sh to fetch models.

Breaks

  • Bit-depth reduction: bitdepth.ipynb (broken with BPDA)
  • JPEG: jpeg.ipynb (broken with BPDA)
  • Cropping: crop.ipynb (broken with EOT)
  • Quilting: quilt.ipynb (broken with EOT+BPDA)
  • Total variation denoising: tv.ipynb (broken with EOT+BPDA)

robustml evaluation

Run with:

python robustml_attack.py --imagenet-path <path> --defense <defense>

Where <defense> is one of bitdepth, jpeg, crop, quilt, or tv.

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