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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 1581–1590 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
SHARP: Search-Based Adversarial Attack for Structured Prediction	Jul 1, 2022	Adversarial AttackDependency Parsing	—Unverified	0	0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples	Apr 16, 2019	Adversarial Attack	—Unverified	0	0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness	Jun 1, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"	Jul 1, 2019	Adversarial AttackAdversarial Defense	—Unverified	0	0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech	Feb 13, 2025	Adversarial AttackAdversarial Attack Detection	—Unverified	0	0
Compressed models are NOT miniature versions of large models	Jul 18, 2024	Adversarial AttackModel Compression	—Unverified	0	0
Compressive Sensing Based Adaptive Defence Against Adversarial Images	Oct 11, 2021	Adversarial AttackCompressive Sensing	—Unverified	0	0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification	Feb 27, 2024	Adversarial AttackAdversarial Attack Detection	—Unverified	0	0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces	Jan 1, 2021	Adversarial AttackSensitivity	—Unverified	0	0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models	Mar 5, 2023	Adversarial AttackManagement	—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