gga-csm 0.0.3

This package provides the implementation of GGA (https://arxiv.org/abs/2102.12196).

GGA-Identifying-Untrustworthy-Predictions

Code relative to "Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis" Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier Paper: https://arxiv.org/abs/2102.12196 Accepted at UAI 2021

We propose a geometric gradient analysis (GGA) of the input gradients of neural networks to detect out-of-distribution data and adversarial attacks. GGA does not require retraining of a given model. Here, we analyze and interpret the gradient of a neural network w.r.t. its input (e.g., saliency map). More precisely, for a given input sample we inspect the geometric relation among all possible saliency maps, calculated for each output class of the model. This is achieved by a pairwise calculation of the cosine similarity between saliency maps. The cosine similarites for a given input can be summarizes with cosine similiarty maps (CSMs)

The following image exemplifies how GGA can be used to differentiate different data types for a MNIST model by calculating the respective CSM for every input.

Examples of CSMs for the "Standard" CIFAR10 model provided by RobustBench libary (https://github.com/RobustBench/robustbench) are given below:

How to use GGA

GGA can be installed with the following command:

Instalation

pip install gga-csm

To compute the cosine similarity map for a given input use the following code:

from gga.CSM import cosine_similarity_maps

csm = cosine_similarity_maps(model, x)

An example is also given in main.py

Citation

If you use our method in a publication please cite:

@article{schwinn2021identifying,
  author    = {Leo Schwinn and
               An Nguyen and
               Ren{\'{e}} Raab and
               Leon Bungert and
               Daniel Tenbrinck and
               Dario Zanca and
               Martin Burger and
               Bjoern Eskofier},
  title     = {Identifying Untrustworthy Predictions in Neural Networks by Geometric
               Gradient Analysis},
  journal   = {CoRR},
  volume    = {abs/2102.12196},
  year      = {2021},
}