Gradient-Based Adversarial and Out-of-Distribution Detection
- URL: http://arxiv.org/abs/2206.08255v1
- Date: Thu, 16 Jun 2022 15:50:41 GMT
- Title: Gradient-Based Adversarial and Out-of-Distribution Detection
- Authors: Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib
- Abstract summary: We introduce confounding labels in gradient generation to probe the effective expressivity of neural networks.
We show that our gradient-based approach allows for capturing the anomaly in inputs based on the effective expressivity of the models.
- Score: 15.510581400494207
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. By 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.
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