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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 1091–1100 of 1808 papers

Title	Date	Tasks	Status	Hype
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing	Feb 16, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack	Feb 15, 2022	Adversarial AttackGraph Learning	—Unverified	0
Attacking c-MARL More Effectively: A Data Driven Approach	Feb 7, 2022	Adversarial AttackMulti-agent Reinforcement Learning	—Unverified	0
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests	Feb 7, 2022	Adversarial AttackVocal Bursts Valence Prediction	CodeCode Available	0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons	Jan 31, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses	Jan 29, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods	Jan 28, 2022	Adversarial AttackExplainable artificial intelligence	—Unverified	0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning	Jan 23, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Robust Unpaired Single Image Super-Resolution of Faces	Jan 22, 2022	Adversarial AttackImage Super-Resolution	—Unverified	0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective	Jan 21, 2022	Adversarial AttackGraph Learning	—Unverified	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