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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 991–1000 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models	Jun 13, 2023	Adversarial AttackDecoder	—Unverified	0	0
Learning Task-aware Robust Deep Learning Systems	Oct 11, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
Is It Time to Redefine the Classification Task for Deep Learning Systems?	Jun 18, 2021	Adversarial AttackClassification	—Unverified	0	0
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition	Sep 11, 2018	Adversarial AttackAutomatic Speech Recognition	—Unverified	0	0
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis	Dec 21, 2020	Adversarial AttackCryptanalysis	—Unverified	0	0
Iterative Adversarial Attack on Image-guided Story Ending Generation	May 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0	0
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition	Sep 19, 2024	Adversarial AttackTraffic Sign Recognition	—Unverified	0	0
Adversarial Rain Attack and Defensive Deraining for DNN Perception	Sep 19, 2020	Adversarial AttackAutonomous Driving	—Unverified	0	0
JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets	Jul 3, 2024	Adversarial AttackSemantic Similarity	—Unverified	0	0
Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts	Nov 15, 2023	Adversarial AttackRed Teaming	—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