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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 911–920 of 1808 papers

Title	Date	Tasks	Status	Hype
Efficient universal shuffle attack for visual object tracking	Mar 14, 2022	Adversarial AttackComputational Efficiency	—Unverified	0
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks	Mar 11, 2022	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	1
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers	Mar 11, 2022	Adversarial AttackSentence	CodeCode Available	0
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity	Mar 10, 2022	Adversarial AttackSemantic Similarity	CodeCode Available	1
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems	Mar 8, 2022	Adversarial AttackSpeech Separation	—Unverified	0
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon	Mar 8, 2022	Adversarial AttackTraffic Sign Recognition	CodeCode Available	1
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art	Mar 7, 2022	Adversarial Attackimage-classification	—Unverified	0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks	Mar 7, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking	Mar 3, 2022	Adversarial AttackObject Tracking	CodeCode Available	1
Adversarial attacks on neural networks through canonical Riemannian foliations	Mar 2, 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