Gradient-Based Adversarial and Out-of-Distribution Detection [article]

Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib
2022 arXiv   pre-print
We propose to utilize gradients for detecting adversarial and out-of-distribution samples. We introduce confounding labels -- labels that differ from normal labels seen during training -- in gradient generation to probe the effective expressivity of neural networks. Gradients depict the amount of change required for a model to properly represent given inputs, providing insight into the representational power of the model established by network architectural properties as well as training data.
more » ... y introducing a label of different design, we remove the dependency on ground truth labels for gradient generation during inference. We show that our gradient-based approach allows for capturing the anomaly in inputs based on the effective expressivity of the models with no hyperparameter tuning or additional processing, and outperforms state-of-the-art methods for adversarial and out-of-distribution detection.
arXiv:2206.08255v2 fatcat:os55tbr46zaitm2sen4pqmcz24