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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 1111–1120 of 1808 papers

Title	Date	Tasks	Status	Hype
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks	Jul 8, 2021	Adversarial AttackBayesian Inference	—Unverified	0
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples	Jul 1, 2021	Adversarial AttackAdversarial Attack Detection	—Unverified	0
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks	Jul 1, 2021	Adversarial Attack	CodeCode Available	0
Bio-Inspired Adversarial Attack Against Deep Neural Networks	Jun 30, 2021	Adversarial AttackObject	—Unverified	0
In-distribution adversarial attacks on object recognition models using gradient-free search	Jun 30, 2021	Adversarial AttackAttribute	CodeCode Available	0
Attack Transferability Characterization for Adversarially Robust Multi-label Classification	Jun 29, 2021	Adversarial AttackClassification	CodeCode Available	0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack	Jun 28, 2021	Adversarial AttackFeature Importance	—Unverified	0
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem	Jun 21, 2021	Adversarial Attack	CodeCode Available	1
Attack to Fool and Explain Deep Networks	Jun 20, 2021	Adversarial AttackImage Generation	—Unverified	0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents	Jun 18, 2021	Adversarial Attackcontinuous-control	—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