Francesco Croce | DeepAI

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∙ 06/22/2023

Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models

While a large amount of work has focused on designing adversarial attack...

0 Francesco Croce, et al. ∙

∙ 03/03/2023

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

While adversarial training has been extensively studied for ResNet archi...

0 Naman D. Singh, et al. ∙

∙ 02/20/2023

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts

Adversarial training is widely used to make classifiers robust to a spec...

0 Francesco Croce, et al. ∙

∙ 02/14/2023

A modern look at the relationship between sharpness and generalization

Sharpness of minima is a promising quantity that can correlate with gene...

0 Maksym Andriushchenko, et al. ∙

∙ 10/21/2022

Diffusion Visual Counterfactual Explanations

Visual Counterfactual Explanations (VCEs) are an important tool to under...

0 Maximilian Augustin, et al. ∙

∙ 10/10/2022

Revisiting adapters with adversarial training

While adversarial training is generally used as a defense mechanism, rec...

1 Sylvestre-Alvise Rebuffi, et al. ∙

∙ 09/14/2022

On the interplay of adversarial robustness and architecture components: patches, convolution and attention

In recent years novel architecture components for image classification h...

0 Francesco Croce, et al. ∙

∙ 05/16/2022

Sparse Visual Counterfactual Explanations in Image Space

Visual counterfactual explanations (VCEs) in image space are an importan...

5 Valentyn Boreiko, et al. ∙

∙ 02/28/2022

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Adaptive defenses that use test-time optimization promise to improve rob...

28 Francesco Croce, et al. ∙

∙ 05/26/2021

Adversarial robustness against multiple l_p-threat models at the price of one and how to quickly fine-tune robust models to another threat model

Adversarial training (AT) in order to achieve adversarial robustness wrt...

0 Francesco Croce, et al. ∙

∙ 03/01/2021

Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers

We show that when taking into account also the image domain [0,1]^d, est...

0 Francesco Croce, et al. ∙

∙ 10/19/2020

RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to ove...

4 Francesco Croce, et al. ∙

∙ 06/23/2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

A large body of research has focused on adversarial attacks which requir...

0 Francesco Croce, et al. ∙

∙ 03/03/2020

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

The field of defense strategies against adversarial attacks has signific...

0 Francesco Croce, et al. ∙

∙ 11/29/2019

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

We propose the Square Attack, a new score-based black-box l_2 and l_∞ ad...

0 Maksym Andriushchenko, et al. ∙

∙ 09/11/2019

Sparse and Imperceivable Adversarial Attacks

Neural networks have been proven to be vulnerable to a variety of advers...

10 Francesco Croce, et al. ∙

∙ 07/03/2019

Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack

The evaluation of robustness against adversarial manipulation of neural ...

0 Francesco Croce, et al. ∙

∙ 05/27/2019

Provable robustness against all adversarial l_p-perturbations for p≥ 1

In recent years several adversarial attacks and defenses have been propo...

0 Francesco Croce, et al. ∙

∙ 03/27/2019

Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks

Modern neural networks are highly non-robust against adversarial manipul...

0 Francesco Croce, et al. ∙

∙ 11/28/2018

A randomized gradient-free attack on ReLU networks

It has recently been shown that neural networks but also other classifie...

0 Francesco Croce, et al. ∙

∙ 10/17/2018

Provable Robustness of ReLU networks via Maximization of Linear Regions

It has been shown that neural network classifiers are not robust. This r...

0 Francesco Croce, et al. ∙

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