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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 941–950 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Improving Network Interpretability via Explanation Consistency Evaluation	Aug 8, 2024	Adversarial Attack	—Unverified	0	0
Improving Neural Network Robustness through Neighborhood Preserving Layers	Jan 28, 2021	Adversarial Attack	—Unverified	0	0
A Framework for Verification of Wasserstein Adversarial Robustness	Oct 13, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator	Sep 13, 2021	Adversarial Attack	—Unverified	0	0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks	Jun 17, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations	Oct 22, 2021	Adversarial Attack	—Unverified	0	0
Improving Robustness of Task Oriented Dialog Systems	Nov 12, 2019	Adversarial AttackData Augmentation	—Unverified	0	0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness	Oct 15, 2019	Adversarial AttackData Augmentation	—Unverified	0	0
A Framework for Understanding Model Extraction Attack and Defense	Jun 23, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection	Jan 1, 2023	Adversarial AttackDynamic neural networks	—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