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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 1181–1190 of 1808 papers

Title	Date	Tasks	Status	Hype
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems	Mar 19, 2021	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	0
KoDF: A Large-scale Korean DeepFake Detection Dataset	Mar 18, 2021	Adversarial AttackDeepFake Detection	—Unverified	0
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection	Mar 17, 2021	Adversarial AttackAutonomous Vehicles	—Unverified	0
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation	Mar 16, 2021	Adversarial AttackObject	CodeCode Available	1
Towards Robust Speech-to-Text Adversarial Attack	Mar 15, 2021	Adversarial AttackRoom Impulse Response (RIR)	—Unverified	0
Generating Unrestricted Adversarial Examples via Three Parameters	Mar 13, 2021	Adversarial Attack	—Unverified	0
Internal Wasserstein Distance for Adversarial Attack and Defense	Mar 13, 2021	Adversarial AttackAdversarial Defense	—Unverified	0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling	Mar 11, 2021	Adversarial Attack	—Unverified	0
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink	Mar 11, 2021	Adversarial Attack	CodeCode Available	1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification	Mar 11, 2021	Adversarial Attack	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