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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 201–210 of 1808 papers

Title	Date	Tasks	Status	Hype
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Meta Gradient Adversarial Attack	Aug 9, 2021	Adversarial AttackMeta-Learning	CodeCode Available	1
Poison Ink: Robust and Invisible Backdoor Attack	Aug 5, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	1
Discriminator-Free Generative Adversarial Attack	Jul 20, 2021	Adversarial AttackDisentanglement	CodeCode Available	1
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem	Jun 21, 2021	Adversarial Attack	CodeCode Available	1
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
On Improving Adversarial Transferability of Vision Transformers	Jun 8, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Transferable Sparse Adversarial Attack	May 31, 2021	Adversarial AttackQuantization	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