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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 531–540 of 1808 papers

Title	Date	Tasks	Status	Hype
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks	Aug 5, 2023	Adversarial Attack	—Unverified	0
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1
Multi-attacks: Many images + the same adversarial attack many target labels	Aug 4, 2023	Adversarial Attack	CodeCode Available	1
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack	Aug 1, 2023	Adversarial AttackHard-label Attack	CodeCode Available	0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness	Aug 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks	Jul 31, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training	Jul 30, 2023	Adversarial Attack	—Unverified	0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning	Jul 27, 2023	Adversarial AttackMulti-Label Learning	CodeCode Available	0
Universal and Transferable Adversarial Attacks on Aligned Language Models	Jul 27, 2023	Adversarial AttackIngenuity	CodeCode Available	4

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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