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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 311–320 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations	Feb 21, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
On Evaluating Adversarial Robustness	Feb 18, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Theoretically Principled Trade-off between Robustness and Accuracy	Jan 24, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Distributionally Adversarial Attack	Aug 16, 2018	Adversarial Attack	CodeCode Available	1
Local Gradients Smoothing: Defense against localized adversarial attacks	Jul 3, 2018	Adversarial Attack	CodeCode Available	1
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations	Jan 24, 2018	Adversarial AttackDepth Estimation	CodeCode Available	1
Towards Deep Learning Models Resistant to Adversarial Attacks	Jun 19, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Examples for Semantic Segmentation and Object Detection	Mar 24, 2017	Adversarial Attackimage-classification	CodeCode Available	1
Deep Variational Information Bottleneck	Dec 1, 2016	Adversarial Attack	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