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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 1221–1230 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning	Jan 25, 2021	Adversarial AttackDNN Testing	CodeCode Available	0
Generating Black-Box Adversarial Examples in Sparse Domain	Jan 22, 2021	Adversarial Attackobject-detection	—Unverified	0
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary	Jan 21, 2021	Adversarial Attackcontinuous-control	CodeCode Available	1
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack	Jan 19, 2021	Adversarial Attack	—Unverified	0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization	Jan 19, 2021	Adversarial Attack	—Unverified	0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions	Jan 17, 2021	Adversarial Attack	—Unverified	0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds	Jan 15, 2021	Adversarial AttackAdversarial Defense	—Unverified	0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series	Jan 13, 2021	Adversarial AttackAdversarial Defense	—Unverified	0
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps	Jan 12, 2021	Adversarial AttackDeep Learning	CodeCode Available	1
Random Transformation of Image Brightness for Adversarial Attack	Jan 12, 2021	Adversarial AttackData Augmentation	CodeCode Available	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