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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 1191–1200 of 1808 papers

Title	Date	Tasks	Status	Hype
Vulnerability of Deep Learning	Mar 16, 2018	Adversarial AttackDeep Learning	—Unverified	0
Wasserstein Adversarial Examples on Univariant Time Series Data	Mar 22, 2023	Adversarial AttackTime Series	—Unverified	0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks	Oct 23, 2019	Adversarial Attackimage-classification	—Unverified	0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation	Dec 20, 2024	Adversarial AttackDiversity	—Unverified	0
Wavelet-Based Image Tokenizer for Vision Transformers	May 28, 2024	Adversarial Attack	—Unverified	0
Wavelets Beat Monkeys at Adversarial Robustness	Apr 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Weighted-Sampling Audio Adversarial Example Attack	Jan 26, 2019	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Weight Map Layer for Noise and Adversarial Attack Robustness	May 2, 2019	Adversarial Attack	—Unverified	0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images	Jun 1, 2020	Adversarial AttackAdversarial Text	—Unverified	0
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective	Jun 14, 2022	Adversarial Attackcounterfactual	—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