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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
Second-Order NLP Adversarial Examples	Oct 5, 2020	Adversarial AttackSemantic Similarity	CodeCode Available	0
A Study for Universal Adversarial Attacks on Texture Recognition	Oct 4, 2020	Adversarial AttackDeep Learning	—Unverified	0
CorrAttack: Black-box Adversarial Attack with Structured Search	Oct 3, 2020	Adversarial AttackBayesian Optimization	—Unverified	0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations	Oct 3, 2020	Adversarial AttackDeep Learning	—Unverified	0
An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood	Oct 2, 2020	Adversarial AttackRetrieval	—Unverified	0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment	Sep 29, 2020	Adversarial Attack	—Unverified	0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations	Sep 29, 2020	Adversarial AttackBilevel Optimization	—Unverified	0
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers	Sep 22, 2020	Adversarial AttackAutonomous Driving	—Unverified	0
Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading	Sep 19, 2020	Adversarial Attack	—Unverified	0
Bias Field Poses a Threat to DNN-based X-Ray Recognition	Sep 19, 2020	Adversarial Attack	—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