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arXiv · 2206.08255

Gradient-Based Adversarial and Out-of-Distribution Detection

Jinsol Lee·Mohit Prabhushankar·Ghassan AlRegib
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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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Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib. 2022-06-16. Gradient-Based Adversarial and Out-of-Distribution Detection. https://arxiv.org/abs/2206.08255

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