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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 921–930 of 1808 papers

Title	Date	Tasks	Status	Hype
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation	Apr 18, 2025	Adversarial Attack	—Unverified	0
QFAL: Quantum Federated Adversarial Learning	Feb 28, 2025	Adversarial AttackFederated Learning	—Unverified	0
Query-Efficient Black-Box Attack by Active Learning	Sep 13, 2018	Active LearningAdversarial Attack	—Unverified	0
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers	Jun 29, 2024	Adversarial Attack	—Unverified	0
Query-Efficient Video Adversarial Attack with Stylized Logo	Aug 22, 2024	Adversarial AttackReinforcement Learning (RL)	—Unverified	0
Query-Free Adversarial Transfer via Undertrained Surrogates	Jul 1, 2020	Adversarial Attack	—Unverified	0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs	Dec 12, 2020	Adversarial AttackGraph Classification	—Unverified	0
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries	Jul 4, 2022	Adversarial AttackFace Recognition	—Unverified	0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis	Dec 11, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors	Dec 14, 2024	Adversarial AttackDeep Reinforcement Learning	—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