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04/05/2023
How to choose your best allies for a transferable attack?
The transferability of adversarial examples is a key issue in the securi...
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03/22/2023
Revisiting DeepFool: generalization and improvement
Deep neural networks have been known to be vulnerable to adversarial exa...
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11/01/2022
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
Although current deep learning techniques have yielded superior performa...
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12/27/2021
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
Despite their impressive performance on image classification tasks, deep...
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12/07/2021
Vehicle trajectory prediction works, but not everywhere
Vehicle trajectory prediction is nowadays a fundamental pillar of self-d...
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08/24/2021
Are socially-aware trajectory prediction models really socially-aware?
Our field has recently witnessed an arms race of neural network-based tr...
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07/24/2021
Adversarial training may be a double-edged sword
Adversarial training has been shown as an effective approach to improve ...
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06/12/2021
What can linearized neural networks actually say about generalization?
For certain infinitely-wide neural networks, the neural tangent kernel (...
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05/07/2021
Uniform Convergence, Adversarial Spheres and a Simple Remedy
Previous work has cast doubt on the general framework of uniform converg...
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05/06/2021
Understanding Catastrophic Overfitting in Adversarial Training
Recently, FGSM adversarial training is found to be able to train a robus...
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04/29/2021
A neural anisotropic view of underspecification in deep learning
The underspecification of most machine learning pipelines means that we ...
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10/19/2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Driven by massive amounts of data and important advances in computationa...
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06/17/2020
Neural Anisotropy Directions
In this work, we analyze the role of the network architecture in shaping...
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03/13/2020
GeoDA: a geometric framework for black-box adversarial attacks
Adversarial examples are known as carefully perturbed images fooling ima...
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02/15/2020
Hold me tight! Influence of discriminative features on deep network boundaries
Important insights towards the explainability of neural networks and the...
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03/26/2019
A geometry-inspired decision-based attack
Deep neural networks have recently achieved tremendous success in image ...
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11/23/2018
Robustness via curvature regularization, and vice versa
State-of-the-art classifiers have been shown to be largely vulnerable to...
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11/06/2018
SparseFool: a few pixels make a big difference
Deep Neural Networks have achieved extraordinary results on image classi...
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02/19/2018
Divide, Denoise, and Defend against Adversarial Attacks
Deep neural networks, although shown to be a successful class of machine...
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12/04/2017
Adaptive Quantization for Deep Neural Network
In recent years Deep Neural Networks (DNNs) have been rapidly developed ...
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11/24/2017
Geometric robustness of deep networks: analysis and improvement
Deep convolutional neural networks have been shown to be vulnerable to a...
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05/26/2017
Analysis of universal adversarial perturbations
Deep networks have recently been shown to be vulnerable to universal per...
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05/26/2017
Classification regions of deep neural networks
The goal of this paper is to analyze the geometric properties of deep ne...
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10/26/2016
Universal adversarial perturbations
Given a state-of-the-art deep neural network classifier, we show the exi...
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08/31/2016
