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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 561–570 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks Neutralization via Data Set Randomization	Jun 21, 2023	Adversarial Attack	—Unverified	0
Sample Attackability in Natural Language Adversarial Attacks	Jun 21, 2023	Adversarial Attack	CodeCode Available	0
Physics-constrained Attack against Convolution-based Human Motion Prediction	Jun 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks	Jun 16, 2023	Adversarial Attack	—Unverified	0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models	Jun 14, 2023	Adversarial AttackMachine Translation	—Unverified	0
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models	Jun 13, 2023	Adversarial AttackDecoder	—Unverified	0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems	Jun 13, 2023	Adversarial AttackFace Swapping	CodeCode Available	0
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models	Jun 9, 2023	Adversarial Attack	—Unverified	0
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions	Jun 9, 2023	Adversarial AttackAtari Games	—Unverified	0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning	Jun 8, 2023	Adversarial AttackIntrusion Detection	—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