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 1431–1440 of 1808 papers

Title	Date	Tasks	Status	Hype
Generating Semantically Valid Adversarial Questions for TableQA	May 26, 2020	Adversarial AttackQuestion Answering	—Unverified	0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks	May 23, 2020	Adversarial AttackClassification	—Unverified	0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning	May 22, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack	May 21, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks	May 20, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
On Intrinsic Dataset Properties for Adversarial Machine Learning	May 19, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective	May 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Universalization of any adversarial attack using very few test examples	May 18, 2020	Adversarial Attack	CodeCode Available	0
Defending Your Voice: Adversarial Attack on Voice Conversion	May 18, 2020	Adversarial AttackVoice Conversion	CodeCode Available	1
Attacking Recommender Systems with Augmented User Profiles	May 17, 2020	Adversarial AttackGenerative Adversarial Network	CodeCode Available	1

Show:10 25 50

← PrevPage 144 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