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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 1301–1310 of 1808 papers

Title	Date	Tasks	Status	Hype
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations	Oct 22, 2021	Adversarial Attack	—Unverified	0
Improving Robustness of Task Oriented Dialog Systems	Nov 12, 2019	Adversarial AttackData Augmentation	—Unverified	0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness	Oct 15, 2019	Adversarial AttackData Augmentation	—Unverified	0
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing	Feb 26, 2024	Adversarial AttackFace Recognition	—Unverified	0
Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation	Feb 24, 2025	Adversarial AttackDiversity	—Unverified	0
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation	Oct 28, 2022	Adversarial AttackFace Recognition	—Unverified	0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing	May 10, 2024	Adversarial Attack	—Unverified	0
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration	Sep 4, 2023	Adversarial AttackFace Recognition	—Unverified	0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE	Aug 25, 2021	Adversarial Attack	—Unverified	0
Influence Based Defense Against Data Poisoning Attacks in Online Learning	Apr 24, 2021	Adversarial AttackData Poisoning	—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