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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 861–870 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Generating Semantic Adversarial Examples via Feature Manipulation	Jan 6, 2020	Adversarial AttackDisentanglement	—Unverified	0	0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense	Nov 22, 2024	Adversarial AttackRobust classification	—Unverified	0	0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning	Dec 9, 2020	Adversarial Attack	—Unverified	0	0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks	Mar 15, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Benchmarking Adversarial Robustness	Dec 26, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding	Feb 24, 2021	Adversarial AttackClassification	—Unverified	0	0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline	Jun 2, 2022	Adversarial Attack	—Unverified	0	0
Generating Black-Box Adversarial Examples in Sparse Domain	Jan 22, 2021	Adversarial Attackobject-detection	—Unverified	0	0
Generating Adversarial Inputs Using A Black-box Differential Technique	Jul 10, 2020	Adversarial Attack	—Unverified	0	0
Generating Adversarial Examples with an Optimized Quality	Jun 30, 2020	Adversarial AttackComputer Security	—Unverified	0	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