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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 1471–1480 of 1808 papers

Title	Date	Tasks	Status	Hype
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection	Oct 7, 2021	Adversarial AttackAndroid Malware Detection	CodeCode Available	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	0
Grey-box Adversarial Attack And Defence For Sentiment Classification	Mar 22, 2021	Adversarial AttackClassification	CodeCode Available	0
Depth-2 Neural Networks Under a Data-Poisoning Attack	May 4, 2020	Adversarial AttackData Poisoning	CodeCode Available	0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies	Nov 26, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Hard-label based Small Query Black-box Adversarial Attack	Mar 9, 2024	Adversarial Attack	CodeCode Available	0
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes	Oct 20, 2019	Adversarial AttackLanguage Modeling	CodeCode Available	0
Unpacking the Resilience of SNLI Contradiction Examples to Attacks	Dec 15, 2024	Adversarial Attack	CodeCode Available	0
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories	Nov 18, 2019	Adversarial Attack	CodeCode Available	0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector	Apr 16, 2018	Adversarial AttackAutonomous Vehicles	CodeCode Available	0

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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