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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 1491–1500 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Attribute-Guided Encryption with Facial Texture Masking	May 22, 2023	Adversarial AttackAttribute	—Unverified	0	0
Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation	Apr 23, 2025	Adversarial Attack	—Unverified	0	0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off	May 21, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking	Jul 18, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0	0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation	Oct 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Adversarial Attacks in Sound Event Classification	Jul 4, 2019	Adversarial AttackClassification	—Unverified	0	0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives	Apr 20, 2024	Adversarial AttackImage Quality Assessment	—Unverified	0	0
Self adversarial attack as an augmentation method for immunohistochemical stainings	Mar 21, 2021	Adversarial AttackImage-to-Image Translation	—Unverified	0	0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing	Jan 1, 2023	Adversarial AttackData Augmentation	—Unverified	0	0
Bias Field Poses a Threat to DNN-based X-Ray Recognition	Sep 19, 2020	Adversarial Attack	—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