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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 151–160 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1	5
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1	5
Adversarial Attack and Defense of Structured Prediction Models	Oct 4, 2020	Adversarial AttackDependency Parsing	CodeCode Available	1	5
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1	5
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios	Feb 10, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Defending Your Voice: Adversarial Attack on Voice Conversion	May 18, 2020	Adversarial AttackVoice Conversion	CodeCode Available	1	5
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink	Mar 11, 2021	Adversarial Attack	CodeCode Available	1	5
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification	Mar 11, 2021	Adversarial Attack	CodeCode Available	1	5

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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