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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 641–650 of 1808 papers

Title	Date	Tasks	Status	Hype
Design of secure and robust cognitive system for malware detection	Aug 3, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach	Jun 1, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning	Jul 28, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training	Jun 17, 2020	Adversarial AttackBilevel Optimization	—Unverified	0
Democratic Training Against Universal Adversarial Perturbations	Feb 8, 2025	Adversarial Attack	—Unverified	0
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified	0
Distillation-Enhanced Physical Adversarial Attacks	Jan 4, 2025	Adversarial AttackKnowledge Distillation	—Unverified	0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks	Jul 31, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing	Aug 22, 2024	Adversarial AttackComputational Efficiency	—Unverified	0
Analyzing the Noise Robustness of Deep Neural Networks	Jan 26, 2020	Adversarial Attack	—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