DeepAI

# Certifying Confidence via Randomized Smoothing

Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top two most-likely classes around an input point under a smoothing distribution to generate a certified radius for a classifier's prediction. However, most smoothing methods do not give us any information about the confidence with which the underlying classifier (e.g., deep neural network) makes a prediction. In this work, we propose a method to generate certified radii for the prediction confidence of the smoothed classifier. We consider two notions for quantifying confidence: average prediction score of a class and the margin by which the average prediction score of one class exceeds that of another. We modify the Neyman-Pearson lemma (a key theorem in randomized smoothing) to design a procedure for computing the certified radius where the confidence is guaranteed to stay above a certain threshold. Our experimental results on CIFAR-10 and ImageNet datasets show that using information about the distribution of the confidence scores allows us to achieve a significantly better certified radius than ignoring it. Thus, we demonstrate that extra information about the base classifier at the input point can help improve certified guarantees for the smoothed classifier.

• 8 publications
• 16 publications
• 67 publications
• 129 publications
02/08/2020

### Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Randomized smoothing, using just a simple isotropic Gaussian distributio...
12/21/2021

### Input-Specific Robustness Certification for Randomized Smoothing

Although randomized smoothing has demonstrated high certified robustness...
06/16/2022

### Double Sampling Randomized Smoothing

Neural networks (NNs) are known to be vulnerable against adversarial per...
02/14/2020

### Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images

We show a hardness result for random smoothing to achieve certified adve...
02/10/2020

### Random Smoothing Might be Unable to Certify ℓ_∞ Robustness for High-Dimensional Images

We show a hardness result for random smoothing to achieve certified adve...
05/12/2022

### Smooth-Reduce: Leveraging Patches for Improved Certified Robustness

Randomized smoothing (RS) has been shown to be a fast, scalable techniqu...
10/24/2019

### Accurate Layerwise Interpretable Competence Estimation

Estimating machine learning performance 'in the wild' is both an importa...