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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 1691–1700 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement	Mar 21, 2022	Adversarial Attack	—Unverified	0	0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications	Nov 22, 2021	Adversarial Attack	—Unverified	0	0
Differentially Private Reward Estimation with Preference Feedback	Oct 30, 2023	Adversarial Attack	—Unverified	0	0
Differential Privacy in Personalized Pricing with Nonparametric Demand Models	Sep 10, 2021	Adversarial Attack	—Unverified	0	0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval	Jan 1, 2021	Adversarial AttackCross-Modal Retrieval	—Unverified	0	0
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking	Mar 21, 2024	Adversarial AttackStyle Transfer	—Unverified	0	0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems	Sep 13, 2021	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking	Jul 17, 2022	Adversarial AttackMiscellaneous	—Unverified	0	0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning	Nov 15, 2022	Adversarial Attackreinforcement-learning	—Unverified	0	0
Applying Tensor Decomposition to image for Robustness against Adversarial Attack	Feb 28, 2020	Adversarial AttackDeep Learning	—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