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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 671–680 of 1808 papers

Title	Date	Tasks	Status	Hype
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security	Feb 2, 2024	Adversarial AttackCollaborative Filtering	—Unverified	0
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack	Jan 30, 2024	Adversarial Attackobject-detection	CodeCode Available	0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation	Jan 28, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Sparse and Transferable Universal Singular Vectors Attack	Jan 25, 2024	Adversarial Attack	—Unverified	0
Exploring Adversarial Threat Models in Cyber Physical Battery Systems	Jan 24, 2024	Adversarial Attack	—Unverified	0
Susceptibility of Adversarial Attack on Medical Image Segmentation Models	Jan 20, 2024	Adversarial AttackImage Segmentation	CodeCode Available	0
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack	Jan 18, 2024	Adversarial AttackStyle Transfer	CodeCode Available	0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack	Jan 18, 2024	Adversarial Attack	—Unverified	0
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems	Jan 17, 2024	Adversarial AttackFace Recognition	—Unverified	0
A Generative Adversarial Attack for Multilingual Text Classifiers	Jan 16, 2024	Adversarial Attack	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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