Publications related to Block-Sparse Adversarial Attack To Fool Transformer-Based Text Classifiers

The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...

Association for Computational Linguistics2020

FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Drazen Dujic, Marko Mogorovic

This paper proposes a novel class of neural-network inspired statistical data-driven models, especially derived for the purpose of design optimization of medium frequency transformers. These models allow for an efficient (3-5 orders of magnitude faster com ...

2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

A geometry-inspired decision-based attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremen-dous success in image classification. Recent studies havehowever shown that they are easily misled into incorrectclassification decisions by adversarial examples. Adver-saries can even craft attacks by que ...

2019

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

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 ...

2019

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

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

Fast and Provable ADMM for Learning with Generative Priors

Volkan Cevher, Armin Eftekhari, Fabian Ricardo Latorre Gomez

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a ...

2019

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

Block-Sparse Adversarial Attack To Fool Transformer-Based Text Classifiers

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The Unstoppable Rise of Computational Linguistics in Deep Learning

FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Square Attack: a query-efficient black-box adversarial attack via random search

A geometry-inspired decision-based attack

Geometry of adversarial robustness of deep networks: methods and applications

A geometry-inspired decision-based attack

A Geometry-Inspired Decision-Based Attack

Biologically plausible deep learning – but how far can we go with shallow networks?

Fast and Provable ADMM for Learning with Generative Priors

Universal Adversarial Attacks on Text Classifiers

The Unstoppable Rise of Computational Linguistics in Deep Learning

A geometry-inspired decision-based attack

A Geometry-Inspired Decision-Based Attack

Geometry of adversarial robustness of deep networks: methods and applications

FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

A geometry-inspired decision-based attack

Biologically plausible deep learning – but how far can we go with shallow networks?

Universal Adversarial Attacks on Text Classifiers

Square Attack: a query-efficient black-box adversarial attack via random search

Fast and Provable ADMM for Learning with Generative Priors