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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 1571–1580 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
CharBot: A Simple and Effective Method for Evading DGA Classifiers	May 3, 2019	Adversarial Attack	—Unverified	0	0
Sequential Attacks on Agents for Long-Term Adversarial Goals	May 31, 2018	Adversarial AttackReinforcement Learning	—Unverified	0	0
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays	Mar 31, 2020	Adversarial AttackGeneral Classification	—Unverified	0	0
Class-Aware Domain Adaptation for Improving Adversarial Robustness	May 10, 2020	Adversarial AttackAdversarial Defense	—Unverified	0	0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words	Aug 1, 2017	Adversarial AttackHate Speech Detection	—Unverified	0	0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey	Jul 1, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0	0
Bidirectional Contrastive Split Learning for Visual Question Answering	Aug 24, 2022	Adversarial AttackBackdoor Attack	—Unverified	0	0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation	Oct 18, 2024	Adversarial AttackDecision Making	—Unverified	0	0
Cloud Adversarial Example Generation for Remote Sensing Image Classification	Sep 21, 2024	Adversarial AttackAdversarial Defense	—Unverified	0	0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples	Jan 1, 2021	Adversarial AttackTransfer Learning	—Unverified	0	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