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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 891–900 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Neon Beam: A Light-based Physical Attack to DNNs	Apr 2, 2022	Adversarial Attack	—Unverified	0
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions	Mar 30, 2022	3D Object DetectionAdversarial Attack	CodeCode Available	1
StyleFool: Fooling Video Classification Systems via Style Transfer	Mar 30, 2022	Adversarial AttackClassification	CodeCode Available	1
Exploring Frequency Adversarial Attacks for Face Forgery Detection	Mar 29, 2022	Adversarial AttackMeta-Learning	—Unverified	0
Zero-Query Transfer Attacks on Context-Aware Object Detectors	Mar 29, 2022	Adversarial AttackObject	—Unverified	0
Boosting Black-Box Adversarial Attacks with Meta Learning	Mar 28, 2022	Adversarial AttackMeta-Learning	—Unverified	0
Text Adversarial Purification as Defense against Adversarial Attacks	Mar 27, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies	Mar 26, 2022	Adversarial Attack	—Unverified	0
Enhancing Transferability of Adversarial Examples with Spatial Momentum	Mar 25, 2022	Adversarial Attack	—Unverified	0
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	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