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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 761–770 of 1808 papers

Title	Date	Tasks	Status	Hype
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers	Sep 22, 2021	Active LearningAdversarial Attack	—Unverified	0
Exploring Adversarial Fake Images on Face Manifold	Jan 9, 2021	Adversarial AttackDeepFake Detection	—Unverified	0
Exploring Adversarial Threat Models in Cyber Physical Battery Systems	Jan 24, 2024	Adversarial Attack	—Unverified	0
Generating Watermarked Adversarial Texts	Oct 25, 2021	Adversarial AttackAdversarial Text	—Unverified	0
Exploring Frequency Adversarial Attacks for Face Forgery Detection	Mar 29, 2022	Adversarial AttackMeta-Learning	—Unverified	0
Exploring High-Order Structure for Robust Graph Structure Learning	Mar 22, 2022	Adversarial AttackGraph structure learning	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0
Exploring the Physical World Adversarial Robustness of Vehicle Detection	Aug 7, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models	May 30, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense	Jan 28, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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