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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 531–540 of 1808 papers

Title	Date	Tasks	Status	Hype
Design of secure and robust cognitive system for malware detection	Aug 3, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Attack-Agnostic Adversarial Detection	Jun 1, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	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
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions	Jun 18, 2021	Adversarial Attack	—Unverified	0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples	Jan 1, 2021	Adversarial AttackTransfer Learning	—Unverified	0
Semantically Stealthy Adversarial Attacks against Segmentation Models	Apr 5, 2021	Adversarial AttackSegmentation	—Unverified	0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples	Apr 16, 2019	Adversarial Attack	—Unverified	0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions	Jan 17, 2021	Adversarial Attack	—Unverified	0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge	May 26, 2021	Adversarial AttackGraph Embedding	—Unverified	0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech	Feb 13, 2025	Adversarial AttackAdversarial Attack Detection	—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