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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 1411–1420 of 1808 papers

Title	Date	Tasks	Status	Hype
Classifier-independent Lower-Bounds for Adversarial Robustness	Jun 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution	Jun 15, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Targeted Adversarial Perturbations for Monocular Depth Prediction	Jun 12, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack	Jun 12, 2020	Adversarial Attack	—Unverified	0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples	Jun 11, 2020	Adversarial Attack	—Unverified	0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors	Jun 11, 2020	Adversarial AttackMedical Image Analysis	CodeCode Available	0
Interpolation between Residual and Non-Residual Networks	Jun 10, 2020	Adversarial Attackimage-classification	CodeCode Available	1
Global Robustness Verification Networks	Jun 8, 2020	Adversarial Attack	—Unverified	0
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection	Jun 5, 2020	Adversarial AttackImage Captioning	CodeCode Available	1

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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