Quantized models introduce discrete rounding operations into the computational graph, which may produce either genuine robustness against inference-time evasion attacks (coarser weight representation changing the decision boundary geometry) or gradient masking (rounding causing zero gradients that blind attacks). The current configuration evaluates pretrained TorchCV ResNet56, WRN-28-10, and DenseNet-100 models on CIFAR-100 across FP32, PTQ, and QAT variants using a layered attack suite. Dataset construction, preprocessing, class count, and TorchCV model identifiers are selected centrally in src/config.py.
To install (most) dependencies
pip install -r requirements.txt
To download datasets (should be placed at root)
CIFAR-10
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
curl -O https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
CIFAR-100
wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
curl -O https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
or set Download=true
To run
python src/QuantAdv.py
To graph incomplete results
python src/combine.py
Obsolete run
python src_old/launcher.py
Notice: You may need to adjust pathing or move the scripts to root for obsolete files.
Results are in ./data
