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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 1401–1410 of 1808 papers

Title	Date	Tasks	Status	Hype
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment	Jul 2, 2020	Adversarial AttackAutonomous Driving	—Unverified	0
Query-Free Adversarial Transfer via Undertrained Surrogates	Jul 1, 2020	Adversarial Attack	—Unverified	0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks	Jul 1, 2020	Adversarial Attack	CodeCode Available	0
Generating Adversarial Examples with an Optimized Quality	Jun 30, 2020	Adversarial AttackComputer Security	—Unverified	0
RayS: A Ray Searching Method for Hard-label Adversarial Attack	Jun 23, 2020	Adversarial AttackHard-label Attack	CodeCode Available	1
Adversarial Attacks for Multi-view Deep Models	Jun 19, 2020	Adversarial Attack	—Unverified	0
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers	Jun 19, 2020	Adversarial AttackLanguage Modeling	CodeCode Available	1
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning	Jun 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions	Jun 18, 2020	Adversarial Attack	CodeCode Available	0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training	Jun 17, 2020	Adversarial AttackBilevel Optimization	—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