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Adversarial Attack

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 181–190 of 1808 papers

Title	Date	Tasks	Status	Hype
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability	Nov 21, 2021	Adversarial Attack	CodeCode Available	1
A Review of Adversarial Attack and Defense for Classification Methods	Nov 18, 2021	Adversarial AttackClassification	CodeCode Available	1
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers	Nov 17, 2021	Adversarial AttackMulti-Object Tracking	CodeCode Available	1
Sparse Adversarial Video Attacks with Spatial Transformations	Nov 10, 2021	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models	Nov 4, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework	Oct 28, 2021	Adversarial AttackLanguage Modelling	CodeCode Available	1
Boosting the Transferability of Video Adversarial Examples via Temporal Translation	Oct 18, 2021	Adversarial AttackTranslation	CodeCode Available	1
Unrestricted Adversarial Attacks on ImageNet Competition	Oct 17, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified