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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 1021–1030 of 1808 papers

Title	Date	Tasks	Status	Hype
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
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models	Oct 17, 2021	Adversarial AttackGraph Neural Network	CodeCode Available	0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction	Oct 16, 2021	Adversarial AttackCombinatorial Optimization	—Unverified	0
Adversarial Attacks on Gaussian Process Bandits	Oct 16, 2021	Adversarial AttackGaussian Processes	CodeCode Available	0
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer	Oct 14, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	1
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools	Oct 14, 2021	Adversarial AttackFairness	—Unverified	0
Adversarial Attack across Datasets	Oct 13, 2021	Adversarial Attackimage-classification	—Unverified	0
A Framework for Verification of Wasserstein Adversarial Robustness	Oct 13, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0

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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