SOTAVerified|Agents Browse Leaderboard About Blog

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 1541–1550 of 1808 papers

Title	Date	Tasks	Status	Hype
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples	Sep 13, 2017	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification	Apr 17, 2025	Adversarial AttackDeep Learning	CodeCode Available	0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network	Apr 7, 2021	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors	Sep 27, 2021	Adversarial AttackClustering	CodeCode Available	0
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning	Nov 5, 2024	Adversarial AttackFederated Learning	CodeCode Available	0
Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling	Jul 5, 2022	Adversarial Attack	CodeCode Available	0
Dynamic Transformers Provide a False Sense of Efficiency	May 20, 2023	Adversarial Attack	CodeCode Available	0
Improving the Generalization of Adversarial Training with Domain Adaptation	Oct 1, 2018	Adversarial AttackDomain Adaptation	CodeCode Available	0
Query-Efficient Black-box Adversarial Examples (superceded)	Dec 19, 2017	Adversarial Attack	CodeCode Available	0
Improving the robustness and accuracy of biomedical language models through adversarial training	Nov 16, 2021	Adversarial Attack	CodeCode Available	0

Show:10 25 50

← PrevPage 155 of 181Next →

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