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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 1011–1020 of 1808 papers

Title	Date	Tasks	Status	Hype
An Actor-Critic Method for Simulation-Based Optimization	Oct 31, 2021	Adversarial AttackReinforcement Learning (RL)	—Unverified	0
AdvCodeMix: Adversarial Attack on Code-Mixed Data	Oct 30, 2021	Adversarial AttackSentence	—Unverified	0
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework	Oct 28, 2021	Adversarial AttackLanguage Modelling	CodeCode Available	1
Disrupting Deep Uncertainty Estimation Without Harming Accuracy	Oct 26, 2021	Adversarial Attack	CodeCode Available	0
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust	Oct 25, 2021	Adversarial Attack	—Unverified	0
Generating Watermarked Adversarial Texts	Oct 25, 2021	Adversarial AttackAdversarial Text	—Unverified	0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations	Oct 22, 2021	Adversarial Attack	—Unverified	0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning	Oct 20, 2021	Adversarial AttackHierarchical Reinforcement Learning	—Unverified	0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information	Oct 19, 2021	Adversarial Attackglobal-optimization	—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