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 751–775 of 1808 papers

Title	Date	Tasks	Status	Hype
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks	Oct 6, 2022	Adversarial AttackDiversity	—Unverified	0
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks	Oct 5, 2022	Adversarial Attack	CodeCode Available	1
Jitter Does Matter: Adapting Gaze Estimation to New Domains	Oct 5, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework	Oct 4, 2022	Adversarial AttackClustering	CodeCode Available	0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers	Oct 4, 2022	Adversarial AttackContrastive Learning	—Unverified	0
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks	Oct 3, 2022	Adversarial Attack	—Unverified	0
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack	Oct 1, 2022	Adversarial AttackDomain Adaptation	—Unverified	0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack	Oct 1, 2022	Adversarial Attack	—Unverified	0
Physical Adversarial Attack meets Computer Vision: A Decade Survey	Sep 30, 2022	Adversarial AttackMedical Diagnosis	CodeCode Available	1
Hiding Visual Information via Obfuscating Adversarial Perturbations	Sep 30, 2022	Adversarial AttackDe-identification	CodeCode Available	1
A Survey on Physical Adversarial Attack in Computer Vision	Sep 28, 2022	Adversarial Attackobject-detection	—Unverified	0
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified	0
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning	Sep 24, 2022	Adversarial Attack	CodeCode Available	1
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified	0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs	Sep 19, 2022	Adversarial Attack	—Unverified	0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction	Sep 19, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models	Sep 17, 2022	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	0
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack	Sep 14, 2022	Adversarial Attackglobal-optimization	CodeCode Available	1
Robust Constrained Reinforcement Learning	Sep 14, 2022	Adversarial Attackreinforcement-learning	—Unverified	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	0
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models	Sep 14, 2022	Adversarial AttackLanguage Modelling	CodeCode Available	1
ADMM based Distributed State Observer Design under Sparse Sensor Attacks	Sep 13, 2022	Adversarial Attack	—Unverified	0
PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models	Sep 13, 2022	Adversarial AttackDeep Learning	—Unverified	0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy	Sep 13, 2022	Adversarial Attack	—Unverified	0
Generate synthetic samples from tabular data	Sep 12, 2022	Adversarial Attack	CodeCode Available	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