SOTAVerified|Agents Browse Leaderboard About

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 721–730 of 1808 papers

Title	Date	Tasks	Status	Hype
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models	Dec 22, 2024	Adversarial AttackFace Recognition	—Unverified	0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech	Feb 13, 2025	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense	Jan 28, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective	May 18, 2025	Adversarial AttackCode Generation	—Unverified	0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples	Apr 16, 2019	Adversarial Attack	—Unverified	0
Evaluating Adversarial Robustness on Document Image Classification	Apr 24, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks	Jul 8, 2021	Adversarial AttackBayesian Inference	—Unverified	0
Evaluating Neural Model Robustness for Machine Comprehension	Apr 1, 2021	Adversarial Attackmodel	—Unverified	0
Adversarial Attack with Pattern Replacement	Nov 25, 2019	Adversarial Attack	—Unverified	0

Show:10 25 50

← PrevPage 73 of 181Next →

All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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