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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 371–380 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	0	5
Adversarial Privacy-preserving Filter	Jul 25, 2020	Adversarial AttackFace Recognition	CodeCode Available	0	5
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning	Aug 1, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	0	5
Adversarial Attack on Network Embeddings via Supervised Network Poisoning	Feb 14, 2021	Adversarial AttackLink Prediction	CodeCode Available	0	5
Role of Spatial Context in Adversarial Robustness for Object Detection	Sep 30, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	0	5
Adversarial Attack on Large Language Models using Exponentiated Gradient Descent	May 14, 2025	Adversarial Attack	CodeCode Available	0	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0	5
A black-box adversarial attack for poisoning clustering	Sep 9, 2020	Adversarial AttackClustering	CodeCode Available	0	5
Adversarial Metric Attack and Defense for Person Re-identification	Jan 30, 2019	Adversarial AttackBenchmarking	CodeCode Available	0	5

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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