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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 931–940 of 1808 papers

Title	Date	Tasks	Status	Hype
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples	May 6, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers	Dec 5, 2023	Adversarial Attack	—Unverified	0
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks	Aug 19, 2022	Adversarial AttackAutonomous Driving	—Unverified	0
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems	Mar 4, 2020	Adversarial AttackRoom Impulse Response (RIR)	—Unverified	0
Real-World Adversarial Examples involving Makeup Application	Sep 4, 2021	Adversarial AttackFace Recognition	—Unverified	0
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering	Dec 17, 2021	Adversarial AttackMulti-hop Question Answering	—Unverified	0
Text Adversarial Purification as Defense against Adversarial Attacks	Mar 27, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack	Feb 15, 2022	Adversarial AttackGraph Learning	—Unverified	0
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements	Feb 18, 2023	Adversarial AttackEthics	—Unverified	0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense	Apr 11, 2023	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