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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 1761–1770 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack on Graph Structured Data	Jun 6, 2018	Adversarial AttackGraph Neural Network	CodeCode Available	0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks	Jun 5, 2018	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization	May 31, 2018	Adversarial Attackimage-classification	—Unverified	0
Sequential Attacks on Agents for Long-Term Adversarial Goals	May 31, 2018	Adversarial AttackReinforcement Learning	—Unverified	0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio	May 30, 2018	Adversarial AttackAudio Compression	—Unverified	0
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization	May 28, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary	May 15, 2018	Adversarial AttackKnowledge Distillation	CodeCode Available	0
ADef: an Iterative Algorithm to Construct Adversarial Deformations	Apr 20, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector	Apr 16, 2018	Adversarial AttackAutonomous Vehicles	CodeCode Available	0
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks	Apr 9, 2018	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