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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 1341–1350 of 1808 papers

Title	Date	Tasks	Status	Hype
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models	Apr 2, 2024	Adversarial AttackImage Generation	—Unverified	0
Jitter Does Matter: Adapting Gaze Estimation to New Domains	Oct 5, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack	Nov 30, 2020	Action RecognitionAdversarial Attack	—Unverified	0
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System	Oct 30, 2024	Adversarial Attack	—Unverified	0
KoDF: A Large-scale Korean DeepFake Detection Dataset	Mar 18, 2021	Adversarial AttackDeepFake Detection	—Unverified	0
Label Smoothing and Adversarial Robustness	Sep 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications	Oct 17, 2019	Adversarial Attack	—Unverified	0
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening	Sep 29, 2021	Adversarial Attack	—Unverified	0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space	May 22, 2023	Adversarial AttackSSIM	—Unverified	0
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions	Jan 1, 2023	Adversarial Attack	—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