Seyed Mohsen Moosavi Dezfooli

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Related publications (26)

Empirical study of the topology and geometry of deep networks

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

The goal of this paper is to analyze the geometric properties of deep neural network image classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. ...

2018

Universal adversarial perturbations

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for co ...

Ieee2017

The Robustness of Deep Networks - A geometric perspective

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

Deep neural networks have recently shown impressive classification performance on a diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is equally important that these classifiers satisfy robustness guarantees: small per ...

Institute of Electrical and Electronics Engineers2017

DeepFool: a simple and accurate method to fool deep neural networks

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

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, well sought, perturbations of the images. Despite the importance of this p ...

2016

Robustness of classifiers: from adversarial to random noise

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

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 empirically observed that these same classifiers are relatively robust to ran ...

2016

Simultaneous Acoustic Localization of Multiple Smartphones with Euclidean Distance Matrices

Yvonne Anne Pignolet-Oswald, Seyed Mohsen Moosavi Dezfooli

In this paper, we present an acoustic localization system for multiple devices. In contrast to systems which localize a device relative to one or several anchor points, we focus on the joint localization of several devices relative to each other. We presen ...

Junction Publishing2016

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