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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 1751–1760 of 1808 papers

Title	Date	Tasks	Status	Hype
Adaptive Adversarial Attack on Scene Text Recognition	Jul 9, 2018	Adversarial Attackimage-classification	—Unverified	0
Local Gradients Smoothing: Defense against localized adversarial attacks	Jul 3, 2018	Adversarial Attack	CodeCode Available	1
Adversarial Examples in Deep Learning: Characterization and Divergence	Jun 29, 2018	Adversarial AttackDeep Learning	—Unverified	0
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples	Jun 27, 2018	Adversarial AttackImage Captioning	CodeCode Available	0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System	Jun 23, 2018	Adversarial AttackFace Recognition	—Unverified	0
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
Sequential Attacks on Agents for Long-Term Adversarial Goals	May 31, 2018	Adversarial AttackReinforcement Learning	—Unverified	0
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization	May 31, 2018	Adversarial Attackimage-classification	—Unverified	0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio	May 30, 2018	Adversarial AttackAudio Compression	—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