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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 1451–1460 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attacks Neutralization via Data Set Randomization	Jun 21, 2023	Adversarial Attack	—Unverified	0	0
Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics	Jan 31, 2025	Adversarial Attack	—Unverified	0	0
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation	Sep 20, 2023	3D Human Pose EstimationAdversarial Attack	—Unverified	0	0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks	Mar 7, 2022	Adversarial AttackAdversarial Defense	—Unverified	0	0
Search Space of Adversarial Perturbations against Image Filters	Mar 5, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Second-Order Adversarial Attack and Certifiable Robustness	May 1, 2019	Adversarial Attack	—Unverified	0	0
A Brief Survey on Deep Learning Based Data Hiding	Mar 2, 2021	Adversarial AttackDeep Learning	—Unverified	0	0
Second-Order NLP Adversarial Examples	Nov 1, 2020	Adversarial AttackSemantic Similarity	—Unverified	0	0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems	Jun 14, 2025	Adversarial AttackHallucination	—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