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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 1031–1040 of 1808 papers

Title	Date	Tasks	Status	Hype
Identification of Attack-Specific Signatures in Adversarial Examples	Oct 13, 2021	Adversarial Attack	—Unverified	0
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning	Oct 13, 2021	Adversarial AttackFederated Learning	CodeCode Available	1
Compressive Sensing Based Adaptive Defence Against Adversarial Images	Oct 11, 2021	Adversarial AttackCompressive Sensing	—Unverified	0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection	Oct 7, 2021	Adversarial AttackAndroid Malware Detection	CodeCode Available	0
Adversarial Attack by Limited Point Cloud Surface Modifications	Oct 7, 2021	Adversarial AttackPoint Cloud Classification	—Unverified	0
A Uniform Framework for Anomaly Detection in Deep Neural Networks	Oct 6, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	0
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack	Oct 6, 2021	Adversarial AttackFace Swapping	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Reversible Attack based on Local Visual Adversarial Perturbation	Oct 6, 2021	Adversarial AttackAutonomous Driving	—Unverified	0
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision	Oct 6, 2021	Adversarial AttackEvent-based vision	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