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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 1581–1590 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization	Sep 23, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex	May 29, 2019	Adversarial AttackBayesian Inference	CodeCode Available	0
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack	Mar 5, 2021	Adversarial AttackDiagnostic	CodeCode Available	0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network	Dec 17, 2021	3D Classification3D Semantic Segmentation	CodeCode Available	0
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics	Mar 9, 2024	Adversarial Attack	CodeCode Available	0
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack	Jun 1, 2021	Adversarial AttackRepresentation Learning	CodeCode Available	0
Dynamic Adversarial Attacks on Autonomous Driving Systems	Dec 10, 2023	Adversarial AttackAutonomous Driving	CodeCode Available	0
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?	Feb 6, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Bitstream Collisions in Neural Image Compression via Adversarial Perturbations	Mar 25, 2025	Adversarial AttackImage Compression	CodeCode Available	0
Stabilized Medical Image Attacks	Mar 9, 2021	Adversarial AttackMedical Diagnosis	CodeCode Available	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