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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 521–530 of 1808 papers

Title	Date	Tasks	Status	Hype
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery	Aug 17, 2023	Adversarial Attack	—Unverified	0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models	Aug 17, 2023	Adversarial AttackStyle Transfer	CodeCode Available	0
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective	Aug 15, 2023	Adversarial Attack	CodeCode Available	0
Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks	Aug 12, 2023	Adversarial AttackAll	—Unverified	0
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook	Aug 11, 2023	Adversarial AttackDepth Estimation	—Unverified	0
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient	Aug 10, 2023	Action RecognitionActivity Recognition	CodeCode Available	1
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning	Aug 8, 2023	Adversarial AttackFederated Learning	—Unverified	0
Exploring the Physical World Adversarial Robustness of Vehicle Detection	Aug 7, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation	Aug 6, 2023	Adversarial AttackDepth Estimation	—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