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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 1711–1720 of 1808 papers

Title	Date	Tasks	Status	Hype
Fooling Network Interpretation in Image Classification	Dec 6, 2018	Adversarial AttackClassification	—Unverified	0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack	Dec 6, 2018	Adversarial Attack	—Unverified	0
Prior Networks for Detection of Adversarial Attacks	Dec 6, 2018	Adversarial AttackAdversarial Attack Detection	—Unverified	0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications	Dec 5, 2018	Adversarial AttackAutonomous Driving	CodeCode Available	0
FineFool: Fine Object Contour Attack via Attention	Dec 1, 2018	Adversarial AttackObject	—Unverified	0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network	Nov 29, 2018	Adversarial AttackFace Recognition	—Unverified	0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Nov 28, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers	Nov 28, 2018	Action RecognitionAdversarial Attack	—Unverified	0
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks	Nov 27, 2018	Adversarial Attack	CodeCode Available	0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies	Nov 26, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	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