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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 751–760 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
A Formalization of Robustness for Deep Neural Networks	Mar 24, 2019	Adversarial Attack	—Unverified	0	0
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings	Jan 31, 2025	Adversarial AttackText Detection	—Unverified	0	0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers	May 11, 2020	Adversarial Attack	—Unverified	0	0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems	May 22, 2025	Adversarial AttackHallucination	—Unverified	0	0
Affine Disentangled GAN for Interpretable and Robust AV Perception	Jul 6, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0	0
Chain Association-based Attacking and Shielding Natural Language Processing Systems	Nov 12, 2024	Adversarial Attack	—Unverified	0	0
AEMIM: Adversarial Examples Meet Masked Image Modeling	Jul 16, 2024	Adversarial AttackRepresentation Learning	—Unverified	0	0
Adversarial Attacks Neutralization via Data Set Randomization	Jun 21, 2023	Adversarial Attack	—Unverified	0	0
Certifiably Robust Variational Autoencoders	Feb 15, 2021	Adversarial AttackDecoder	—Unverified	0	0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation	Apr 19, 2024	Adversarial AttackAdversarial Defense	—Unverified	0	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