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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 711–720 of 1808 papers

Title	Date	Tasks	Status	Hype
Foiling Explanations in Deep Neural Networks	Nov 27, 2022	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
SAGA: Spectral Adversarial Geometric Attack on 3D Meshes	Nov 24, 2022	Adversarial Attack	CodeCode Available	1
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
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack	Nov 21, 2022	Activity RecognitionAdversarial Attack	CodeCode Available	1
Ignore Previous Prompt: Attack Techniques For Language Models	Nov 17, 2022	Adversarial AttackAdversarial Text	CodeCode Available	2
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation	Nov 16, 2022	Adversarial AttackDiversity	CodeCode Available	0
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection	Nov 16, 2022	Adversarial AttackModel Optimization	CodeCode Available	1
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack	Nov 15, 2022	Adversarial Attack	CodeCode Available	1

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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