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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 1011–1020 of 1808 papers

Title	Date	Tasks	Status	Hype
Scaling Laws for Black box Adversarial Attacks	Nov 25, 2024	Adversarial Attack	—Unverified	0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks	Mar 7, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Search Space of Adversarial Perturbations against Image Filters	Mar 5, 2020	Adversarial AttackDeep Learning	—Unverified	0
Second-Order Adversarial Attack and Certifiable Robustness	May 1, 2019	Adversarial Attack	—Unverified	0
Second-Order NLP Adversarial Examples	Nov 1, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems	Jun 14, 2025	Adversarial AttackHallucination	—Unverified	0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability	Apr 7, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples	Sep 7, 2022	Adversarial Attack	—Unverified	0
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks	Feb 14, 2018	Adversarial Attack	—Unverified	0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack	Mar 3, 2020	Adversarial Attack	—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