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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 1431–1440 of 1808 papers

Title	Date	Tasks	Status	Hype
FireBERT: Hardening BERT-based classifiers against adversarial attack	Aug 10, 2020	Adversarial Attack	CodeCode Available	0
Visual Attack and Defense on Text	Aug 7, 2020	Adversarial Attack	—Unverified	0
Stabilizing Deep Tomographic Reconstruction	Aug 4, 2020	Adversarial Attackcompressed sensing	—Unverified	0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks	Aug 3, 2020	Adversarial AttackComputational Efficiency	—Unverified	0
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
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing	Jul 27, 2020	Adversarial AttackAttribute	—Unverified	0
From Sound Representation to Model Robustness	Jul 27, 2020	Adversarial AttackAdversarial Robustness	—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