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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 1081–1090 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors	Apr 19, 2022	Adversarial AttackDeepFake Detection	—Unverified	0	0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy	Jun 4, 2023	Adversarial AttackAttribute	—Unverified	0	0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency	Apr 16, 2024	Adversarial Attack	—Unverified	0	0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs	Jun 23, 2022	Adversarial AttackAutonomous Driving	—Unverified	0	0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs	Dec 28, 2021	Adversarial AttackCombinatorial Optimization	—Unverified	0	0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy	Nov 10, 2019	Adversarial AttackAtari Games	—Unverified	0	0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping	Apr 23, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0	0
Minority Reports Defense: Defending Against Adversarial Patches	Apr 28, 2020	Adversarial AttackGeneral Classification	—Unverified	0	0
Adversarial training with perturbation generator networks	Sep 25, 2019	Adversarial Attack	—Unverified	0	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