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07/27/2023
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
Similarity metrics have played a significant role in computer vision to ...
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06/16/2023
Towards Better Certified Segmentation via Diffusion Models
The robustness of image segmentation has been an important research topi...
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05/25/2023
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Neural networks are known to be susceptible to adversarial samples: smal...
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05/25/2023
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Since the control of the Lipschitz constant has a great impact on the tr...
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03/06/2023
A Unified Algebraic Perspective on Lipschitz Neural Networks
Important research efforts have focused on the design and training of ne...
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06/03/2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
Randomized smoothing is the dominant standard for provable defenses agai...
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10/25/2021
Scalable Lipschitz Residual Networks with Convex Potential Flows
The Lipschitz constant of neural networks has been established as a key ...
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09/02/2021
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
Deep neural networks are state-of-the-art in a wide variety of tasks, ho...
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12/04/2020
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to pro...
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06/15/2020
Fast Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization
This paper tackles the problem of Lipschitz regularization of Convolutio...
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03/25/2019
Robust Neural Networks using Randomized Adversarial Training
Since the discovery of adversarial examples in machine learning, researc...
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02/04/2019
Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family
This paper investigates the theory of robustness against adversarial att...
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01/29/2019
On the Expressive Power of Deep Fully Circulant Neural Networks
In this paper, we study deep fully circulant neural networks, that is de...
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10/02/2018