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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 1741–1750 of 1808 papers

Title	Date	Tasks	Status	Hype
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning	Jul 27, 2023	Adversarial AttackMulti-Label Learning	CodeCode Available	0
ADef: an Iterative Algorithm to Construct Adversarial Deformations	Apr 20, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
Attack Transferability Characterization for Adversarially Robust Multi-label Classification	Jun 29, 2021	Adversarial AttackClassification	CodeCode Available	0
Adaptive Image Transformations for Transfer-based Adversarial Attack	Nov 27, 2021	Adversarial Attack	CodeCode Available	0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy	Jul 8, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory prediction	Apr 19, 2024	Adversarial AttackPrediction	CodeCode Available	0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications	Dec 5, 2018	Adversarial AttackAutonomous Driving	CodeCode Available	0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers	Jul 4, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples	Feb 1, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning	May 5, 2021	Adversarial AttackAnomaly Detection	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