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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 1161–1170 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
One-Index Vector Quantization Based Adversarial Attack on Image Classification	Sep 2, 2024	Adversarial Attackimage-classification	—Unverified	0	0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help	Oct 10, 2023	Adversarial AttackDiversity	—Unverified	0	0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention	Jun 1, 2020	Adversarial AttackObject	—Unverified	0	0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers	Apr 24, 2020	Adversarial Attack	—Unverified	0	0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs	Apr 2, 2022	Adversarial Attack	—Unverified	0	0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System	Oct 31, 2019	Adversarial AttackReal-World Adversarial Attack	—Unverified	0	0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective	Feb 19, 2023	Adversarial AttackBackdoor Attack	—Unverified	0	0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Oct 24, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Nov 28, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models	Feb 14, 2024	Adversarial AttackClassification	—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