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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 611–620 of 1808 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense	Jul 17, 2020	Adversarial AttackAdversarial Defense	—Unverified	0
Adversarial defenses via a mixture of generators	Oct 5, 2021	Adversarial Attack	—Unverified	0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training	Feb 20, 2023	Adversarial Attack	—Unverified	0
Adversarial Defense based on Structure-to-Signal Autoencoders	Mar 21, 2018	Adversarial AttackAdversarial Defense	—Unverified	0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences	Feb 24, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks	Jun 5, 2018	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Data Encryption	Feb 10, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms	Sep 15, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs	Sep 19, 2022	Adversarial Attack	—Unverified	0
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models	May 17, 2019	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