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 521–530 of 1808 papers

Title	Date	Tasks	Status	Hype
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples	May 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarially Robust Classification by Conditional Generative Model Inversion	Jan 12, 2022	Adversarial AttackClassification	—Unverified	0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval	Jan 1, 2021	Adversarial AttackCross-Modal Retrieval	—Unverified	0
Adversarial Learning of Deepfakes in Accounting	Oct 9, 2019	Adversarial AttackERP	—Unverified	0
Attacking Perceptual Similarity Metrics	May 15, 2023	Adversarial AttackExperimental Design	—Unverified	0
Attacking Important Pixels for Anchor-free Detectors	Jan 26, 2023	Adversarial Attackobject-detection	—Unverified	0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain	Jul 5, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation	Mar 13, 2024	Adversarial AttackConditional Image Generation	—Unverified	0
Enhancing Transformation-based Defenses using a Distribution Classifier	Jun 1, 2019	Adversarial Attack	—Unverified	0
Post-train Black-box Defense via Bayesian Boundary Correction	Jun 29, 2023	Activity RecognitionAdversarial Attack	—Unverified	0

Show:10 25 50

← PrevPage 53 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