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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 911–920 of 1808 papers

Title	Date	Tasks	Status	Hype
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing	Jan 1, 2023	Adversarial AttackData Augmentation	—Unverified	0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
ExploreADV: Towards exploratory attack for Neural Networks	Jan 1, 2023	Adversarial Attack	—Unverified	0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives	Jan 1, 2023	Adversarial AttackMulti-Object Tracking	—Unverified	0
Angelic Patches for Improving Third-Party Object Detector Performance	Jan 1, 2023	Adversarial AttackObject	CodeCode Available	0
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions	Jan 1, 2023	Adversarial Attack	—Unverified	0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection	Jan 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0
Towards Transferable Targeted Adversarial Examples	Jan 1, 2023	Adversarial Attack	CodeCode Available	0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients	Jan 1, 2023	Adversarial Attack	—Unverified	0
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence	Dec 31, 2022	Adversarial Attack	—Unverified	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