Seyed Mohsen Moosavi Dezfooli

Publications associées (26)

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Redundant features can hurt robustness to distribution shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

Robustness via curvature regularization, and vice versa

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

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 training. In this paper, we investigate the effect of adversarial training on the ...

IEEE2019

Universal Adversarial Attacks on Text Classifiers

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Melika Behjati

Despite the vast success neural networks have achieved in different application domains, they have been proven to be vulnerable to adversarial perturbations (small changes in the input), which lead them to produce the wrong output. In this paper, we propos ...

IEEE2019

A Geometry-Inspired Decision-Based Attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by que ...

IEEE COMPUTER SOC2019

SparseFool: a few pixels make a big difference

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas

Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image's pixe ...

IEEE2019

Geometry of adversarial robustness of deep networks: methods and applications

Seyed Mohsen Moosavi Dezfooli

We are witnessing a rise in the popularity of using artificial neural networks in many fields of science and technology. Deep neural networks in particular have shown impressive classification performance on a number of challenging benchmarks, generally in ...

EPFL2019

Adaptive Quantization for Deep Neural Network

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yiren Zhou

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

SparseFool: a few pixels make a big difference

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas

2018

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysi ...

2018

Geometric robustness of deep networks: analysis and improvement

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Can Kanbak

Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simpl ...

2018

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