121 citations · 505 across the 31 of their papers we have counts for
18 papers · 1 filter
On the Anisotropy of Score-Based Generative Models
Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti
We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce the Score Anisotropy Directions (…
Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost Asymmetry
Mahdi Salmani, Alireza Abdollahpoorrostam, Seyed-Mohsen Moosavi-Dezfooli
Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of qu…
LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
Borna Khodabandeh, Amirabbas Afzali, Amirhossein Afsharrad +4
Visual encoders have become fundamental components in modern computer vision pipelines. However, ensuring robustness against adversarial perturbations remains a critical challenge.…
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
Sajad Movahedi, Antonio Orvieto, Seyed-Mohsen Moosavi-Dezfooli
In this paper, we propose the , which argues that the input space curvature of a neural network remains invariant under transformati…
Revisiting DeepFool: generalization and improvement
Alireza Abdollahpoorrostam, Mahed Abroshan, Seyed-Mohsen Moosavi-Dezfooli
Deep neural networks have been known to be vulnerable to adversarial examples, which are inputs that are modified slightly to fool the network into making incorrect predictions. Th…
Adversarial training may be a double-edged sword
Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Huaiyu Dai
Adversarial training has been shown as an effective approach to improve the robustness of image classifiers against white-box attacks. However, its effectiveness against black-box…
What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK onl…
Understanding Catastrophic Overfitting in Adversarial Training
Peilin Kang, Seyed-Mohsen Moosavi-Dezfooli
Recently, FGSM adversarial training is found to be able to train a robust model which is comparable to the one trained by PGD but an order of magnitude faster. However, there is a…
Uniform Convergence, Adversarial Spheres and a Simple Remedy
Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann
Previous work has cast doubt on the general framework of uniform convergence and its ability to explain generalization in neural networks. By considering a specific dataset, it was…
A neural anisotropic view of underspecification in deep learning
Guillermo Ortiz-Jimenez, Itamar Franco Salazar-Reque, Apostolos Modas +2
The underspecification of most machine learning pipelines means that we cannot rely solely on validation performance to assess the robustness of deep learning systems to naturally…
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli +1
Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications…
Neural Anisotropy Directions
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli +1
In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., c…
Hold me tight! Influence of discriminative features on deep network boundaries
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli +1
Important insights towards the explainability of neural networks reside in the characteristics of their decision boundaries. In this work, we borrow tools from the field of adversa…
A geometry-inspired decision-based attack
Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification…
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato +1
State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial traini…
Adaptive Quantization for Deep Neural Network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung +1
In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNN…
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been e…
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard
State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small…