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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 931–940 of 1808 papers

Title	Date	Tasks	Status	Hype
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification	Nov 29, 2022	Adversarial AttackClassification	—Unverified	0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning	Nov 29, 2022	Adversarial AttackContinual Learning	—Unverified	0
Foiling Explanations in Deep Neural Networks	Nov 27, 2022	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale	Nov 23, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples	Nov 22, 2022	Adversarial AttackPoint Cloud Classification	—Unverified	0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation	Nov 16, 2022	Adversarial AttackDiversity	CodeCode Available	0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning	Nov 15, 2022	Adversarial Attackreinforcement-learning	—Unverified	0
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation	Nov 15, 2022	Adversarial Attack	—Unverified	0
Generating Textual Adversaries with Minimal Perturbation	Nov 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