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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 1491–1500 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack on Deep Product Quantization Network for Image Retrieval	Feb 26, 2020	Adversarial AttackImage Retrieval	—Unverified	0
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition	Feb 22, 2020	Adversarial AttackGait Recognition	—Unverified	0
A Bayes-Optimal View on Adversarial Examples	Feb 20, 2020	Adversarial Attack	—Unverified	0
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent	Feb 18, 2020	Adversarial Attackimage-classification	CodeCode Available	0
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack	Feb 17, 2020	Adversarial Attack	—Unverified	0
Undersensitivity in Neural Reading Comprehension	Feb 15, 2020	Adversarial AttackData Augmentation	—Unverified	0
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization	Feb 12, 2020	Adversarial AttackNeural Architecture Search	CodeCode Available	1
Adversarial Data Encryption	Feb 10, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks	Feb 10, 2020	Adversarial AttackDeblurring	CodeCode Available	1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1

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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