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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 981–990 of 1808 papers

Title	Date	Tasks	Status	Hype
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples	Sep 7, 2022	Adversarial Attack	—Unverified	0
Impact of Scaled Image on Robustness of Deep Neural Networks	Sep 2, 2022	Adversarial Attackimage-classification	—Unverified	0
A Black-Box Attack on Optical Character Recognition Systems	Aug 30, 2022	Adversarial AttackBinarization	—Unverified	0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm	Aug 25, 2022	Adversarial Attack	—Unverified	0
Bidirectional Contrastive Split Learning for Visual Question Answering	Aug 24, 2022	Adversarial AttackBackdoor Attack	—Unverified	0
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection	Aug 23, 2022	Adversarial Attack	CodeCode Available	0
Different Spectral Representations in Optimized Artificial Neural Networks and Brains	Aug 22, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment	Aug 19, 2022	Adversarial AttackGrammatical Error Correction	—Unverified	0
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks	Aug 19, 2022	Adversarial AttackAutonomous Driving	—Unverified	0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search	Aug 17, 2022	Adversarial 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