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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 1311–1320 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries	Jul 4, 2022	Adversarial AttackFace Recognition	—Unverified	0	0
Adversarial Data Encryption	Feb 10, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis	Dec 11, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0	0
Weight Map Layer for Noise and Adversarial Attack Robustness	May 2, 2019	Adversarial Attack	—Unverified	0	0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs	Sep 19, 2022	Adversarial Attack	—Unverified	0	0
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors	Dec 14, 2024	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things	Oct 2, 2023	Adversarial AttackFederated Learning	—Unverified	0	0
Adversarial Body Shape Search for Legged Robots	May 20, 2022	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach	Jun 1, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	0
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples	May 6, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	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