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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 981–990 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Investigating Decision Boundaries of Trained Neural Networks	Aug 7, 2019	Adversarial Attack	—Unverified	0	0
The FEVER2.0 Shared Task	Nov 1, 2019	Adversarial Attack	—Unverified	0	0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization	Oct 30, 2019	Adversarial AttackIntrusion Detection	—Unverified	0	0
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems	Jan 26, 2021	Activity RecognitionAdversarial Attack	—Unverified	0	0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning	May 22, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0	0
A Formalization of Robustness for Deep Neural Networks	Mar 24, 2019	Adversarial Attack	—Unverified	0	0
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries	Nov 1, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
Affine Disentangled GAN for Interpretable and Robust AV Perception	Jul 6, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0	0
AEMIM: Adversarial Examples Meet Masked Image Modeling	Jul 16, 2024	Adversarial AttackRepresentation Learning	—Unverified	0	0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models	Jan 31, 2023	Adversarial AttackMachine Reading Comprehension	—Unverified	0	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