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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 621–630 of 1808 papers

Title	Date	Tasks	Status	Hype
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations	Jan 9, 2025	Activity RecognitionAdversarial Attack	—Unverified	0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks	Nov 20, 2020	Adversarial Attack	—Unverified	0
Detecting and Segmenting Adversarial Graphics Patterns from Images	Aug 20, 2021	Adversarial AttackSegmentation	—Unverified	0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System	Jan 23, 2025	Adversarial AttackFace Recognition	—Unverified	0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies	Aug 16, 2024	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things	Oct 2, 2023	Adversarial AttackFederated Learning	—Unverified	0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security	Feb 2, 2024	Adversarial AttackCollaborative Filtering	—Unverified	0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks	Jun 5, 2018	Adversarial AttackAdversarial Robustness	—Unverified	0
3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving	Jul 14, 2025	3DGSAdversarial Attack	—Unverified	0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack	Dec 24, 2024	Adversarial AttackFederated Learning	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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