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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 221–230 of 1808 papers

Title	Date	Tasks	Status	Hype
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification	Mar 11, 2021	Adversarial Attack	CodeCode Available	1
BASAR:Black-box Attack on Skeletal Action Recognition	Mar 9, 2021	Action RecognitionActivity Recognition	CodeCode Available	1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack	Mar 9, 2021	Action RecognitionAdversarial Attack	CodeCode Available	1
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain	Mar 4, 2021	Adversarial Attack	CodeCode Available	1
A Survey On Universal Adversarial Attack	Mar 2, 2021	Adversarial AttackSurvey	CodeCode Available	1
Online Adversarial Attacks	Mar 2, 2021	Adversarial Attack	CodeCode Available	1
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits	Feb 21, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	1
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning	Feb 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary	Jan 21, 2021	Adversarial Attackcontinuous-control	CodeCode Available	1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps	Jan 12, 2021	Adversarial AttackDeep Learning	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