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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 1601–1610 of 1808 papers

Title	Date	Tasks	Status	Hype
Residue-Based Natural Language Adversarial Attack Detection	Apr 17, 2022	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Resilience of Named Entity Recognition Models under Adversarial Attack	Jul 1, 2022	Adversarial Attacknamed-entity-recognition	CodeCode Available	0
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs	Jun 16, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations	Jun 5, 2023	Adversarial Attack	CodeCode Available	0
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary	May 15, 2018	Adversarial AttackKnowledge Distillation	CodeCode Available	0
Adversarial and Clean Data Are Not Twins	Apr 17, 2017	Adversarial Attack	CodeCode Available	0
Adversarial Training for Physics-Informed Neural Networks	Oct 18, 2023	Adversarial Attack	CodeCode Available	0
Accelerated Stochastic Gradient-free and Projection-free Methods	Jul 16, 2020	Adversarial Attack	CodeCode Available	0
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization	Sep 11, 2022	Adversarial AttackAutonomous Navigation	CodeCode Available	0
XSS Adversarial Attacks Based on Deep Reinforcement Learning: A Replication and Extension Study	Feb 26, 2025	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	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