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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 1631–1640 of 1808 papers

Title	Date	Tasks	Status	Hype
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses	Jun 1, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness	Jun 1, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	May 31, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Real-Time Adversarial Attacks	May 31, 2019	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
Identifying Classes Susceptible to Adversarial Attacks	May 30, 2019	Adversarial Attack	—Unverified	0
Bandlimiting Neural Networks Against Adversarial Attacks	May 30, 2019	Adversarial Attack	—Unverified	0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness	May 30, 2019	Adversarial Attack	—Unverified	0
Functional Adversarial Attacks	May 29, 2019	Adversarial Attack	CodeCode Available	0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex	May 29, 2019	Adversarial AttackBayesian Inference	CodeCode Available	0
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization	May 29, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	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