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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 431–440 of 1808 papers

Title	Date	Tasks	Status	Hype
AutoAugment Input Transformation for Highly Transferable Targeted Attacks	Dec 21, 2023	Adversarial Attack	—Unverified	0
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples	Dec 21, 2023	Adversarial Attackcounterfactual	CodeCode Available	0
Mutual-modality Adversarial Attack with Semantic Perturbation	Dec 20, 2023	Adversarial Attack	—Unverified	0
A Malware Classification Survey on Adversarial Attacks and Defences	Dec 15, 2023	Adversarial AttackClassification	—Unverified	0
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks	Dec 15, 2023	Adversarial AttackAutonomous Driving	—Unverified	0
Towards Transferable Targeted 3D Adversarial Attack in the Physical World	Dec 15, 2023	Adversarial AttackNeRF	CodeCode Available	1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection	Dec 14, 2023	Adversarial AttackAttribute	CodeCode Available	1
Forbidden Facts: An Investigation of Competing Objectives in Llama-2	Dec 14, 2023	Adversarial Attack	—Unverified	0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification	Dec 13, 2023	Adversarial AttackEntity Typing	CodeCode Available	0
Towards Transferable Adversarial Attacks with Centralized Perturbation	Dec 11, 2023	Adversarial Attack	—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