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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 1351–1360 of 1808 papers

Title	Date	Tasks	Status	Hype
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization	Dec 14, 2020	Adversarial Attack	CodeCode Available	0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs	Dec 12, 2020	Adversarial AttackGraph Classification	—Unverified	0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis	Dec 11, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning	Dec 9, 2020	Adversarial Attack	—Unverified	0
Towards Natural Robustness Against Adversarial Examples	Dec 4, 2020	Adversarial Attack	—Unverified	0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers	Dec 3, 2020	Adversarial Attack	—Unverified	0
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available	0
Adversarial Attacks on Deep Graph Matching	Dec 1, 2020	Adversarial AttackDensity Estimation	—Unverified	0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack	Dec 1, 2020	Adversarial Attackslot-filling	—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