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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 791–800 of 1808 papers

Title	Date	Tasks	Status	Hype
Feature Unlearning for Pre-trained GANs and VAEs	Mar 10, 2023	Adversarial Attack	—Unverified	0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods	Jan 28, 2022	Adversarial AttackExplainable artificial intelligence	—Unverified	0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems	Oct 8, 2022	Adversarial AttackFederated Learning	—Unverified	0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks	Dec 21, 2023	Adversarial Attack	—Unverified	0
Global Robustness Verification Networks	Jun 8, 2020	Adversarial Attack	—Unverified	0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN	Nov 12, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Defense Against Explanation Manipulation	Nov 8, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives	Jan 1, 2023	Adversarial AttackMulti-Object Tracking	—Unverified	0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection	Jun 30, 2023	Adversarial AttackDeep Learning	—Unverified	0
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks	May 1, 2025	Adversarial Attackregression	—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