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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 1511–1520 of 1808 papers

Title	Date	Tasks	Status	Hype
Search Space of Adversarial Perturbations against Image Filters	Mar 5, 2020	Adversarial AttackDeep Learning	—Unverified	0
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems	Mar 4, 2020	Adversarial AttackRoom Impulse Response (RIR)	—Unverified	0
Double Backpropagation for Training Autoencoders against Adversarial Attack	Mar 4, 2020	Adversarial AttackRobust classification	—Unverified	0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack	Mar 3, 2020	Adversarial Attack	—Unverified	0
Applying Tensor Decomposition to image for Robustness against Adversarial Attack	Feb 28, 2020	Adversarial AttackDeep Learning	—Unverified	0
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

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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