Does the code implement the robust-ml API and include pre-trained models: {yes}
Dataset: {ImageNet, CIFAR-10}
Threat model: {ℓ∞(ϵ=8/255)}
Natural accuracy: {77.32% on ImageNet, 92.55% on CIFAR-10}
Claims: {76.06% on ImageNet, 88.41% on CIFAR-10}
Note: {These are the new results and haven't updated to the paper in arXiv yet. The code is up to date. We have tested the robust-ml API implementation on CIFAR-10 but haven't tested on ImageNet (didn't find the "val.txt" file the data provider needs). We just assumed data provider provides data in (N, H, W, C) arrangement.}
Name: {Bandlimiting Neural Networks Against Adversarial Attacks}
Authors: {Yuping Lin, Kasra Ahmadi K. A., Hui Jiang}
Paper: {https://arxiv.org/abs/1905.12797}
Code: {https://github.com/YupingLin171/PostAvgDefense}
Venue: {N/A}
Does the code implement the robust-ml API and include pre-trained models: {yes}
Dataset: {ImageNet, CIFAR-10}
Threat model: {ℓ∞(ϵ=8/255)}
Natural accuracy: {77.32% on ImageNet, 92.55% on CIFAR-10}
Claims: {76.06% on ImageNet, 88.41% on CIFAR-10}
Note: {These are the new results and haven't updated to the paper in arXiv yet. The code is up to date. We have tested the robust-ml API implementation on CIFAR-10 but haven't tested on ImageNet (didn't find the "val.txt" file the data provider needs). We just assumed data provider provides data in (N, H, W, C) arrangement.}