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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 1121–1130 of 1808 papers

Title	Date	Tasks	Status	Hype
Is It Time to Redefine the Classification Task for Deep Learning Systems?	Jun 18, 2021	Adversarial AttackClassification	—Unverified	0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions	Jun 18, 2021	Adversarial Attack	—Unverified	0
Limited Budget Adversarial Attack Against Online Image Stream	Jun 18, 2021	Adversarial Attack	—Unverified	0
Light Lies: Optical Adversarial Attack	Jun 18, 2021	Adversarial AttackClassification	—Unverified	0
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology	Jun 14, 2021	Adversarial AttackDiagnostic	—Unverified	0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models	Jun 13, 2021	Adversarial AttackNatural Language Understanding	—Unverified	0
TDGIA:Effective Injection Attacks on Graph Neural Networks	Jun 12, 2021	Adversarial Attack	CodeCode Available	1
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm	Jun 10, 2021	Adversarial Attack	CodeCode Available	0
On Improving Adversarial Transferability of Vision Transformers	Jun 8, 2021	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