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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 1701–1710 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Application of Adversarial Examples to Physical ECG Signals	Aug 20, 2021	Adversarial AttackECG Classification	—Unverified	0	0
Physical-World Optical Adversarial Attacks on 3D Face Recognition	May 26, 2022	Adversarial AttackFace Recognition	—Unverified	0	0
Sparse and Transferable Universal Singular Vectors Attack	Jan 25, 2024	Adversarial Attack	—Unverified	0	0
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate	May 1, 2021	Adversarial Attack	—Unverified	0	0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training	Jun 17, 2020	Adversarial AttackBilevel Optimization	—Unverified	0	0
White-Box Target Attack for EEG-Based BCI Regression Problems	Nov 7, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization	Apr 11, 2022	Adversarial AttackObject	—Unverified	0	0
Classifier-independent Lower-Bounds for Adversarial Robustness	Jun 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Distillation-Enhanced Physical Adversarial Attacks	Jan 4, 2025	Adversarial AttackKnowledge Distillation	—Unverified	0	0
Semantically Stealthy Adversarial Attacks against Segmentation Models	Apr 5, 2021	Adversarial AttackSegmentation	—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