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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 741–750 of 1808 papers

Title	Date	Tasks	Status	Hype
Defensive Quantization: When Efficiency Meets Robustness	Apr 17, 2019	Adversarial AttackCPU	—Unverified	0
EvolBA: Evolutionary Boundary Attack under Hard-label Black Box condition	Jul 2, 2024	Adversarial Attack	—Unverified	0
Adversarial Attack with Raindrops	Feb 28, 2023	Adversarial AttackGenerative Adversarial Network	—Unverified	0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2	Dec 14, 2023	Adversarial Attack	—Unverified	0
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition	Jul 19, 2021	Adversarial AttackFace Recognition	—Unverified	0
Attacking Perceptual Similarity Metrics	May 15, 2023	Adversarial AttackExperimental Design	—Unverified	0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection	Feb 4, 2025	Adversarial AttackFraud Detection	—Unverified	0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment	Aug 19, 2022	Adversarial AttackGrammatical Error Correction	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—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