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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 701–710 of 1808 papers

Title	Date	Tasks	Status	Hype
Object-fabrication Targeted Attack for Object Detection	Dec 13, 2022	Adversarial AttackObject	—Unverified	0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection	Dec 13, 2022	Active LearningAdversarial Attack	—Unverified	0
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design	Dec 12, 2022	Adversarial Attack	CodeCode Available	1
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments	Dec 11, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization	Dec 9, 2022	Adversarial Attack	CodeCode Available	0
Targeted Adversarial Attacks against Neural Network Trajectory Predictors	Dec 8, 2022	Adversarial AttackTrajectory Forecasting	—Unverified	0
Pareto Regret Analyses in Multi-objective Multi-armed Bandit	Dec 1, 2022	Adversarial Attack	—Unverified	0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification	Nov 29, 2022	Adversarial AttackClassification	—Unverified	0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning	Nov 29, 2022	Adversarial AttackContinual Learning	—Unverified	0
Imperceptible Adversarial Attack via Invertible Neural Networks	Nov 28, 2022	Adversarial Attack	CodeCode Available	1

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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