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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 1691–1700 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense	Mar 5, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Adversarial Attack and Defense on Point Sets	Feb 28, 2019	Adversarial Attack	—Unverified	0
On the Effectiveness of Low Frequency Perturbations	Feb 28, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust Decision Trees Against Adversarial Examples	Feb 27, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch	Feb 20, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
There are No Bit Parts for Sign Bits in Black-Box Attacks	Feb 19, 2019	Adversarial Attack	—Unverified	0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems	Feb 12, 2019	Adversarial AttackGeneral Classification	—Unverified	0
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?	Feb 6, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Optimal Attack against Autoregressive Models by Manipulating the Environment	Feb 1, 2019	Adversarial AttackModel Predictive Control	—Unverified	0
The Efficacy of SHIELD under Different Threat Models	Feb 1, 2019	Adversarial Attackimage-classification	—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