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 1601–1610 of 1808 papers

Title	Date	Tasks	Status	Hype
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack	Sep 24, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection	Sep 20, 2019	Adversarial AttackMalware Detection	—Unverified	0
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation	Sep 19, 2019	Adversarial AttackExplainable Artificial Intelligence (XAI)	—Unverified	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms	Sep 15, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Natural Language Adversarial Defense through Synonym Encoding	Sep 15, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Adversarial Attack on Skeleton-based Human Action Recognition	Sep 14, 2019	Action RecognitionAdversarial Attack	—Unverified	0
An Empirical Investigation of Randomized Defenses against Adversarial Attacks	Sep 12, 2019	Adversarial AttackGeneral Classification	CodeCode Available	0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification	Sep 10, 2019	Adversarial AttackGeneral Classification	—Unverified	0
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available	0

Show:10 25 50

← PrevPage 161 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