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 311–320 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attack and Defense of Structured Prediction Models	Oct 4, 2020	Adversarial AttackDependency Parsing	CodeCode Available	1	5
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution	Oct 31, 2022	Adversarial AttackSentence	CodeCode Available	1	5
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks	Jun 1, 2022	Adversarial AttackGPU	CodeCode Available	1	5
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	CodeCode Available	1	5
Generalizable Black-Box Adversarial Attack with Meta Learning	Jan 1, 2023	Adversarial AttackMeta-Learning	CodeCode Available	1	5
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
On Intrinsic Dataset Properties for Adversarial Machine Learning	May 19, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval	Apr 18, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Robust Deep Reinforcement Learning through Adversarial Loss	Aug 5, 2020	Adversarial AttackAtari Games	CodeCode Available	1	5
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection	Feb 19, 2023	Adversarial Attack	CodeCode Available	1	5

Show:10 25 50

← PrevPage 32 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