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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 1401–1410 of 1808 papers

Title	Date	Tasks	Status	Hype
Controversial stimuli: pitting neural networks against each other as models of human recognition	Nov 21, 2019	Adversarial Attack	CodeCode Available	0
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs	Mar 20, 2024	Adversarial Attack	CodeCode Available	0
Foiling Explanations in Deep Neural Networks	Nov 27, 2022	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate	Apr 14, 2023	Adversarial Attack	CodeCode Available	0
ColorFool: Semantic Adversarial Colorization	Nov 25, 2019	Adversarial AttackColorization	CodeCode Available	0
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	0
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	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