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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 751–760 of 1808 papers

Title	Date	Tasks	Status	Hype
Generating Adversarial Attacks in the Latent Space	Apr 10, 2023	Adversarial AttackGenerative Adversarial Network	—Unverified	0
Adversarially Robust Classification by Conditional Generative Model Inversion	Jan 12, 2022	Adversarial AttackClassification	—Unverified	0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples	May 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples	Feb 8, 2021	Adversarial Attack	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense	Jan 28, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks	Dec 21, 2020	Adversarial AttackDimensionality Reduction	—Unverified	0
ExploreADV: Towards exploratory attack for Neural Networks	Jan 1, 2023	Adversarial Attack	—Unverified	0
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks	Jul 8, 2021	Adversarial AttackBayesian Inference	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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