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 1461–1470 of 1808 papers

Title	Date	Tasks	Status	Hype
TransFool: An Adversarial Attack against Neural Machine Translation Models	Feb 2, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	0
Translate your gibberish: black-box adversarial attack on machine translation systems	Mar 20, 2023	Adversarial AttackMachine Translation	CodeCode Available	0
Graph Adversarial Immunization for Certifiable Robustness	Feb 16, 2023	Adversarial AttackCombinatorial Optimization	CodeCode Available	0
Graph-based methods coupled with specific distributional distances for adversarial attack detection	May 31, 2023	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Adversarial Attack on Large Language Models using Exponentiated Gradient Descent	May 14, 2025	Adversarial Attack	CodeCode Available	0
Physics-constrained Attack against Convolution-based Human Motion Prediction	Jun 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Adversarial Attack on Graph Structured Data	Jun 6, 2018	Adversarial AttackGraph Neural Network	CodeCode Available	0
Graph Neural Network Explanations are Fragile	Jun 5, 2024	Adversarial AttackGraph Neural Network	CodeCode Available	0
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models	Oct 19, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack	Oct 14, 2020	Adversarial Attack	CodeCode Available	0

Show:10 25 50

← PrevPage 147 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