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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 1131–1140 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain	May 9, 2019	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
Vulnerability of Deep Learning	Mar 16, 2018	Adversarial AttackDeep Learning	—Unverified	0	0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack	Sep 29, 2021	Adversarial Attack	—Unverified	0	0
Adversarial Relighting Against Face Recognition	Aug 18, 2021	Adversarial AttackFace Recognition	—Unverified	0	0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition	Dec 21, 2020	Adversarial AttackAutomatic Speech Recognition	—Unverified	0	0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer	May 15, 2024	Adversarial AttackAutomatic Speech Recognition	—Unverified	0	0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs	Sep 29, 2021	Adversarial AttackObject Recognition	—Unverified	0	0
Wasserstein Adversarial Examples on Univariant Time Series Data	Mar 22, 2023	Adversarial AttackTime Series	—Unverified	0	0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task	Feb 22, 2024	Adversarial AttackContrastive Learning	—Unverified	0	0
Adversarial Attack for Asynchronous Event-based Data	Dec 27, 2021	Adversarial AttackAutonomous Driving	—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