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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 351–360 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
AdjointDEIS: Efficient Gradients for Diffusion Models	May 23, 2024	Adversarial Attack	CodeCode Available	0	5
Foiling Explanations in Deep Neural Networks	Nov 27, 2022	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0	5
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks	Apr 18, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0	5
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification	Jul 8, 2023	Adversarial AttackPerson Re-Identification	CodeCode Available	0	5
Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition	Oct 25, 2023	Adversarial AttackLicense Plate Detection	CodeCode Available	0	5
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization	Dec 14, 2020	Adversarial Attack	CodeCode Available	0	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	0	5
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models	May 16, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0	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