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 661–670 of 1808 papers

Title	Date	Tasks	Status	Hype
TextDefense: Adversarial Text Detection based on Word Importance Entropy	Feb 12, 2023	Adversarial AttackAdversarial Text	—Unverified	0
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend	Feb 6, 2023	Adversarial Attack	—Unverified	0
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks	Feb 4, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification	Feb 3, 2023	Adversarial AttackClassification	—Unverified	0
TransFool: An Adversarial Attack against Neural Machine Translation Models	Feb 2, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks	Feb 1, 2023	Adversarial Attack	—Unverified	0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models	Jan 31, 2023	Adversarial AttackMachine Reading Comprehension	—Unverified	0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example	Jan 30, 2023	Adversarial Attack	—Unverified	0
Identifying Adversarially Attackable and Robust Samples	Jan 30, 2023	Active LearningAdversarial Attack	CodeCode Available	0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense	Jan 28, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0

Show:10 25 50

← PrevPage 67 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