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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 1461–1470 of 1808 papers

Title	Date	Tasks	Status	Hype
Headless Horseman: Adversarial Attacks on Transfer Learning Models	Apr 20, 2020	Adversarial AttackTransfer Learning	—Unverified	0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning	Apr 19, 2020	Adversarial AttackDialogue Generation	—Unverified	0
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space	Apr 17, 2020	Active LearningAdversarial Attack	—Unverified	0
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	0
Towards Transferable Adversarial Attack against Deep Face Recognition	Apr 13, 2020	Adversarial AttackFace Recognition	—Unverified	0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images	Apr 11, 2020	Adversarial AttackSegmentation	—Unverified	0
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking	Apr 8, 2020	Adversarial AttackPerson Re-Identification	CodeCode Available	1
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras	Apr 4, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	0
BAE: BERT-based Adversarial Examples for Text Classification	Apr 4, 2020	Adversarial AttackAdversarial Text	CodeCode Available	2

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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