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 651–660 of 1808 papers

Title	Date	Tasks	Status	Hype
Boosting Adversarial Transferability using Dynamic Cues	Feb 23, 2023	Adversarial Attack	—Unverified	0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training	Feb 20, 2023	Adversarial Attack	—Unverified	0
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System	Feb 20, 2023	Adversarial Attack	—Unverified	0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective	Feb 19, 2023	Adversarial AttackBackdoor Attack	—Unverified	0
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection	Feb 19, 2023	Adversarial Attack	CodeCode Available	1
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning	Feb 18, 2023	Adversarial AttackCross-Domain Few-Shot	CodeCode Available	1
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements	Feb 18, 2023	Adversarial AttackEthics	—Unverified	0
Robust Mid-Pass Filtering Graph Convolutional Networks	Feb 16, 2023	Adversarial AttackNode Classification	CodeCode Available	1
Graph Adversarial Immunization for Certifiable Robustness	Feb 16, 2023	Adversarial AttackCombinatorial Optimization	CodeCode Available	0
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images	Feb 13, 2023	Adversarial AttackObject	CodeCode Available	0

Show:10 25 50

← PrevPage 66 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