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 1381–1390 of 1808 papers

Title	Date	Tasks	Status	Hype
From Sound Representation to Model Robustness	Jul 27, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Privacy-preserving Filter	Jul 25, 2020	Adversarial AttackFace Recognition	CodeCode Available	0
T-BFA: Targeted Bit-Flip Adversarial Weight Attack	Jul 24, 2020	Adversarial AttackCPU	CodeCode Available	0
Robust Tracking against Adversarial Attacks	Jul 20, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Immunization for Certifiable Robustness on Graphs	Jul 19, 2020	Adversarial AttackBilevel Optimization	CodeCode Available	1
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering	Jul 19, 2020	Adversarial AttackData Augmentation	CodeCode Available	1
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection	Jul 18, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense	Jul 17, 2020	Adversarial AttackAdversarial Defense	—Unverified	0
Backdoor Learning: A Survey	Jul 17, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	2

Show:10 25 50

← PrevPage 139 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