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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 321–330 of 1808 papers

Title	Date	Tasks	Status	Hype
A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment	May 27, 2025	Adversarial AttackAdversarial Defense	—Unverified	0
A Formalization of Robustness for Deep Neural Networks	Mar 24, 2019	Adversarial Attack	—Unverified	0
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings	Jan 31, 2025	Adversarial AttackText Detection	—Unverified	0
Affine Disentangled GAN for Interpretable and Robust AV Perception	Jul 6, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0
AEMIM: Adversarial Examples Meet Masked Image Modeling	Jul 16, 2024	Adversarial AttackRepresentation Learning	—Unverified	0
Adversarial Attacks Neutralization via Data Set Randomization	Jun 21, 2023	Adversarial Attack	—Unverified	0
AdvCodeMix: Adversarial Attack on Code-Mixed Data	Oct 30, 2021	Adversarial AttackSentence	—Unverified	0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation	Apr 19, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception	Feb 12, 2025	Adversarial AttackAutonomous Driving	—Unverified	0
Adversarial Attacks in Sound Event Classification	Jul 4, 2019	Adversarial AttackClassification	—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