kondiz / adv_framing

Repository for the paper "Adversarial Framing for Image and Video Classification"

Adversarial Framing

This is the official implementation of the experiments from the paper "Adversarial Framing for Image and Video Classification" (video) by Michał Zając, Konrad Żołna, Negar Rostamzadeh and Pedro Pinheiro.

The code from the paper "Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?" is also included in deps/resnets_3d folder, as we attack the model from that paper.

Our code was originally forked from Classifier-agnostic saliency map extraction repository.

Requirements

The code uses Python 3 and packages listed in requirements.txt. If you use pip, you can install them by pip install -r requirements.txt.

Datasets preparation

Imagenet

1. Follow the instructions to download and unpack the dataset.
2. Set environment variable IMAGENET_DATA_DIR with the directory to the dataset by export IMAGENET_DATA_DIR=/your/imagenet/dir, where /your/imagenet/dir should contain train and val folders (as in the instructions above).

UCF101

1. Follow the instructions to download, unpack and preprocess the dataset.
2. Set data environment variables UCF101_DATA_DIR and UCF101_ANNOTATION_PATH.

• export UCF101_DATA_DIR=/your/data/dir, where /your/data/dir is jpg_video_directory from the instruction above.
• export UCF101_ANNOTATION_PATH=/your/annotation/path, where /your/annotation/path is a path to the file ucf101_01.json created with the above instruction.

3. Download a pretrained model called resnext-101-kinetics-ucf101_split1.pth from here. The model comes from the paper Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
4. Set model environment variable UCF101_MODEL by export UCF101_MODEL=/your/model/path.

Running

• First run export PYTHONPATH=$PYTHONPATH:deps from the main project directory.
• To reproduce untargeted ImageNet experiments, run python3 main.py --dataset imagenet --width $WIDTH --epochs 5 --lr 0.1 --lr-decay-wait 2 --lr-decay-coefficient 0.1, where you should set WIDTH of the framing.
• To reproduce untargeted UCF101 experiments, run python3 main.py --dataset ucf101 --width $WIDTH --epochs 60 --lr 0.03 --lr-decay-wait 15 --lr-decay-coefficient 0.3, where you should set WIDTH of the framing.
• To draw some examples of attacks on ImageNet, run python3 draw_examples_imagenet.py --framing $CHECKPOINT. As a CHECKPOINT you can use some model from pretrained directory.

Citation

If you found this code useful, please use the following citation:

@paper{zajac2019framing,
  title={Adversarial Framing for Image and Video Classification},
  author={Zaj\k{a}c, Micha\l{} and \.Zo\l{}na, Konrad and Rostamzadeh, Negar and Pinheiro, Pedro},
  conference={AAAI Conference on Artificial Intelligence},
  year={2019}
}