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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 691–700 of 1808 papers

Title	Date	Tasks	Status	Hype
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
Efficient universal shuffle attack for visual object tracking	Mar 14, 2022	Adversarial AttackComputational Efficiency	—Unverified	0
EFSG: Evolutionary Fooling Sentences Generator	Oct 12, 2020	Adversarial AttackBinary Classification	—Unverified	0
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks	Dec 15, 2023	Adversarial AttackAutonomous Driving	—Unverified	0
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features	Jun 28, 2024	Adversarial Attack	—Unverified	0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks	Sep 29, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model	Apr 26, 2020	Adversarial Attack	—Unverified	0
Energy Attack: On Transferring Adversarial Examples	Sep 9, 2021	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