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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 811–820 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Examples for Model-Based Control: A Sensitivity Analysis	Jul 14, 2022	Adversarial AttackSensitivity	—Unverified	0
Perturbation Inactivation Based Adversarial Defense for Face Recognition	Jul 13, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Frequency Domain Model Augmentation for Adversarial Attack	Jul 12, 2022	Adversarial AttackDiversity	CodeCode Available	1
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy	Jul 8, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network	Jul 7, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling	Jul 5, 2022	Adversarial Attack	CodeCode Available	0
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing	Jul 5, 2022	Adversarial AttackImitation Learning	CodeCode Available	0
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries	Jul 4, 2022	Adversarial AttackFace Recognition	—Unverified	0
Resilience of Named Entity Recognition Models under Adversarial Attack	Jul 1, 2022	Adversarial Attacknamed-entity-recognition	CodeCode Available	0
SHARP: Search-Based Adversarial Attack for Structured Prediction	Jul 1, 2022	Adversarial AttackDependency Parsing	—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