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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 771–780 of 1808 papers

Title	Date	Tasks	Status	Hype
Extreme Miscalibration and the Illusion of Adversarial Robustness	Feb 27, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models	May 17, 2025	Adversarial AttackWeather Forecasting	—Unverified	0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images	Nov 28, 2020	Adversarial AttackAdversarial Defense	—Unverified	0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning	Nov 4, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense	Nov 22, 2024	Adversarial AttackRobust classification	—Unverified	0
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations	Sep 26, 2024	Adversarial AttackSensitivity	—Unverified	0
Fall Leaf Adversarial Attack on Traffic Sign Classification	Nov 27, 2024	Adversarial AttackClassification	—Unverified	0
Audio Adversarial Examples: Attacks Using Vocal Masks	Feb 4, 2021	Adversarial AttackSpeech-to-Text	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense	Jan 28, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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