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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 1131–1140 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Robust Speech-to-Text Adversarial Attack	Mar 15, 2021	Adversarial AttackRoom Impulse Response (RIR)	—Unverified	0
Towards Sybil Resilience in Decentralized Learning	Jun 26, 2023	Adversarial AttackFederated Learning	—Unverified	0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods	Apr 18, 2023	Adversarial AttackAutonomous Driving	—Unverified	0
Towards Transferable Adversarial Attack against Deep Face Recognition	Apr 13, 2020	Adversarial AttackFace Recognition	—Unverified	0
Towards Transferable Adversarial Attacks with Centralized Perturbation	Dec 11, 2023	Adversarial Attack	—Unverified	0
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks	Oct 13, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models	Jan 26, 2021	3D Object DetectionAdversarial Attack	—Unverified	0
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence	Dec 31, 2022	Adversarial Attack	—Unverified	0
Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images	Feb 23, 2025	Adversarial AttackQuestion Answering	—Unverified	0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning	Nov 29, 2022	Adversarial AttackContinual Learning	—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