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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 611–620 of 1808 papers

Title	Date	Tasks	Status	Hype
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection	Apr 11, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Generating Adversarial Attacks in the Latent Space	Apr 10, 2023	Adversarial AttackGenerative Adversarial Network	—Unverified	0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack	Apr 8, 2023	Adversarial AttackAttribute	—Unverified	0
GradMDM: Adversarial Attack on Dynamic Networks	Apr 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0
To be Robust and to be Fair: Aligning Fairness with Robustness	Mar 31, 2023	Adversarial AttackFairness	—Unverified	0
Fooling the Image Dehazing Models by First Order Gradient	Mar 30, 2023	Adversarial AttackImage Dehazing	CodeCode Available	1
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion	Mar 29, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Class-Conditioned Transformation for Enhanced Robust Image Classification	Mar 27, 2023	Adversarial AttackClassification	CodeCode Available	0
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Effective black box adversarial attack with handcrafted kernels	Mar 24, 2023	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