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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 1541–1550 of 1808 papers

Title	Date	Tasks	Status	Hype
Scratch that! An Evolution-based Adversarial Attack against Neural Networks	Dec 5, 2019	Adversarial AttackImage Captioning	CodeCode Available	0
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds	Dec 1, 2019	Adversarial AttackClassify 3D Point Clouds	CodeCode Available	0
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components	Dec 1, 2019	Adversarial AttackClassification	CodeCode Available	0
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	Nov 28, 2019	Adversarial AttackDomain Adaptation	CodeCode Available	0
Towards Security Threats of Deep Learning Systems: A Survey	Nov 28, 2019	Adversarial AttackDeep Learning	—Unverified	0
ColorFool: Semantic Adversarial Colorization	Nov 25, 2019	Adversarial AttackColorization	CodeCode Available	0
Adversarial Attack with Pattern Replacement	Nov 25, 2019	Adversarial Attack	—Unverified	0
Time-aware Gradient Attack on Dynamic Network Link Prediction	Nov 24, 2019	Adversarial AttackLink Prediction	—Unverified	0
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction	Nov 22, 2019	Adversarial Attackimage-classification	CodeCode Available	0
Heuristic Black-box Adversarial Attacks on Video Recognition Models	Nov 21, 2019	Adversarial AttackVideo Recognition	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