Adversarial Attack

590 papers with code • 2 benchmarks • 9 datasets

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Libraries

Use these libraries to find Adversarial Attack models and implementations

Most implemented papers

Foolbox: A Python toolbox to benchmark the robustness of machine learning models

bethgelab/foolbox 13 Jul 2017

Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models.

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

ysharma1126/EAD-Attack 13 Sep 2017

Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify.

Adversarial Training for Free!

mahyarnajibi/FreeAdversarialTraining NeurIPS 2019

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

huanzhang12/ZOO-Attack 14 Aug 2017

However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples.

Local Gradients Smoothing: Defense against localized adversarial attacks

Muzammal-Naseer/NRP 3 Jul 2018

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i. e., carefully perturbed inputs designed to mislead the network at inference time.

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies

BaoWangMath/EnResNet NeurIPS 2019

However, both natural and robust accuracies, in classifying clean and adversarial images, respectively, of the trained robust models are far from satisfactory.

Real-world adversarial attack on MTCNN face detection system

edosedgar/mtcnnattack 14 Oct 2019

Recent studies proved that deep learning approaches achieve remarkable results on face detection task.

Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers

derryhub/fairmot-attack 17 Nov 2021

Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers.

Certified Defenses against Adversarial Examples

worksheets/0xa21e7940 ICLR 2018

While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs.

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

anishathalye/obfuscated-gradients ICML 2018

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.