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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 1751–1760 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Metric Attack and Defense for Person Re-identification	Jan 30, 2019	Adversarial AttackBenchmarking	CodeCode Available	0
Deep generative models as an adversarial attack strategy for tabular machine learning	Sep 19, 2024	Adversarial Attack	CodeCode Available	0
DeepFool: a simple and accurate method to fool deep neural networks	Nov 14, 2015	Adversarial AttackGeneral Classification	CodeCode Available	0
A Theoretical View of Linear Backpropagation and Its Convergence	Dec 21, 2021	Adversarial Attack	CodeCode Available	0
Training Meta-Surrogate Model for Transferable Adversarial Attack	Sep 5, 2021	Adversarial Attackmodel	CodeCode Available	0
On Detecting Adversarial Perturbations	Feb 14, 2017	Adversarial AttackBinary Classification	CodeCode Available	0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA	Nov 5, 2020	Adversarial AttackCloud Computing	CodeCode Available	0
Safety Verification of Deep Neural Networks	Oct 21, 2016	Adversarial AttackAdversarial Defense	CodeCode Available	0
A Targeted Universal Attack on Graph Convolutional Network	Nov 29, 2020	Adversarial AttackGraph Neural Network	CodeCode Available	0
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses	Jun 1, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	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