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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 1371–1380 of 1808 papers

Title	Date	Tasks	Status	Hype
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks	Aug 3, 2020	Adversarial AttackComputational Efficiency	—Unverified	0
Sparse Adversarial Attack via Perturbation Factorization	Aug 1, 2020	Adversarial Attack	CodeCode Available	1
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing	Aug 1, 2020	Adversarial AttackAttribute	CodeCode Available	1
SimAug: Learning Robust Representations from Simulation for Trajectory Prediction	Aug 1, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator	Jul 31, 2020	Adversarial Attack	—Unverified	0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs	Jul 30, 2020	Adversarial Attack	—Unverified	0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data	Jul 29, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning	Jul 28, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Attacking and Defending Machine Learning Applications of Public Cloud	Jul 27, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	2
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing	Jul 27, 2020	Adversarial AttackAttribute	—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