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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 971–980 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning	Dec 16, 2021	Adversarial AttackImage Compression	—Unverified	0
NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction Operation	Dec 14, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	0
Triangle Attack: A Query-efficient Decision-based Adversarial Attack	Dec 13, 2021	Adversarial AttackDimensionality Reduction	CodeCode Available	1
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare	Dec 11, 2021	Adversarial AttackPosition	—Unverified	0
Learning to Learn Transferable Attack	Dec 10, 2021	Adversarial AttackData Augmentation	CodeCode Available	0
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework	Dec 10, 2021	Adversarial AttackAutonomous Driving	CodeCode Available	0
Amicable Aid: Perturbing Images to Improve Classification Performance	Dec 9, 2021	Adversarial AttackClassification	—Unverified	0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization	Dec 8, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks	Dec 6, 2021	Adversarial AttackData Poisoning	—Unverified	0
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness	Dec 5, 2021	Adversarial AttackAdversarial Defense	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