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 1381–1390 of 1808 papers

Title	Date	Tasks	Status	Hype
Second-Order NLP Adversarial Examples	Nov 1, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
TextAttack: Lessons learned in designing Python frameworks for NLP	Nov 1, 2020	Adversarial AttackData Augmentation	—Unverified	0
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification	Oct 30, 2020	Adversarial AttackGeneral Classification	—Unverified	0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?	Oct 29, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers	Oct 22, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
Learning Black-Box Attackers with Transferable Priors and Query Feedback	Oct 21, 2020	Adversarial Attack	CodeCode Available	0
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set	Oct 20, 2020	Adversarial Attack	—Unverified	0
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning	Oct 15, 2020	Adversarial Attack	—Unverified	0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing	Oct 15, 2020	Adversarial AttackData Poisoning	—Unverified	0

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