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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 671–680 of 1808 papers

Title	Date	Tasks	Status	Hype
Defensive Quantization: When Efficiency Meets Robustness	Apr 17, 2019	Adversarial AttackCPU	—Unverified	0
Adversarial Attack with Raindrops	Feb 28, 2023	Adversarial AttackGenerative Adversarial Network	—Unverified	0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning	Apr 19, 2020	Adversarial AttackDialogue Generation	—Unverified	0
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications	Nov 22, 2021	Adversarial Attack	—Unverified	0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation	Dec 28, 2023	Adversarial AttackNode Classification	—Unverified	0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks	Oct 6, 2022	Adversarial AttackDiversity	—Unverified	0
Adversarial Examples in Deep Learning: Characterization and Divergence	Jun 29, 2018	Adversarial AttackDeep Learning	—Unverified	0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods	Jan 28, 2022	Adversarial AttackExplainable artificial intelligence	—Unverified	0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples	Feb 8, 2021	Adversarial Attack	—Unverified	0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers	Sep 22, 2021	Active LearningAdversarial Attack	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Acc	90.1	90.1(1)	Community Verified
2	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
3	ResNet20	Test Acc	89.5	90.1(1)	Community Verified
4	Xu et al.	Attack: PGD20	78.68	—	Unverified
5	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
6	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
7	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
8	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
9	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Acc	80.4	—	Community Verified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
3	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified