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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 1531–1540 of 1808 papers

Title	Date	Tasks	Status	Hype
Improved Network Robustness with Adversary Critic	Oct 30, 2018	Adversarial Attack	CodeCode Available	0
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior	May 29, 2024	Adversarial AttackBayesian Optimization	CodeCode Available	0
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack	Apr 3, 2019	Adversarial Attack	CodeCode Available	0
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks	Nov 10, 2020	Adversarial AttackBayesian Inference	CodeCode Available	0
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models	Oct 11, 2023	Adversarial AttackDenoising	CodeCode Available	0
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature	May 2, 2023	Adversarial Attack	CodeCode Available	0
Robustness for Non-Parametric Classification: A Generic Attack and Defense	Jun 7, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Pyramid Adversarial Training Improves ViT Performance	Nov 30, 2021	Adversarial AttackData Augmentation	CodeCode Available	0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems	Mar 19, 2021	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	0
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges	Mar 24, 2023	Adversarial AttackMedical Diagnosis	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