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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 1081–1090 of 1808 papers

Title	Date	Tasks	Status	Hype
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training	Mar 14, 2022	Adversarial AttackClassification	—Unverified	0
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers	Mar 11, 2022	Adversarial AttackSentence	CodeCode Available	0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems	Mar 8, 2022	Adversarial AttackSpeech Separation	—Unverified	0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks	Mar 7, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art	Mar 7, 2022	Adversarial Attackimage-classification	—Unverified	0
Detecting Adversarial Perturbations in Multi-Task Perception	Mar 2, 2022	Adversarial AttackDepth Estimation	CodeCode Available	0
Adversarial attacks on neural networks through canonical Riemannian foliations	Mar 2, 2022	Adversarial Attack	CodeCode Available	0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey	Feb 22, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness	Feb 18, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias	Feb 18, 2022	Adversarial AttackBackdoor Attack	—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