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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 1771–1780 of 1808 papers

Title	Date	Tasks	Status	Hype
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language Models	Mar 25, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	0
On Robustness of Neural Ordinary Differential Equations	Oct 12, 2019	Adversarial Attack	CodeCode Available	0
Data-Driven Falsification of Cyber-Physical Systems	May 6, 2025	Adversarial AttackAutonomous Vehicles	CodeCode Available	0
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems	Mar 27, 2019	Adversarial AttackSentence	CodeCode Available	0
DAmageNet: A Universal Adversarial Dataset	Dec 16, 2019	Adversarial Attack	CodeCode Available	0
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method	Jul 26, 2019	Adversarial AttackBayesian Optimization	CodeCode Available	0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion	Oct 3, 2024	Adversarial AttackDenoising	CodeCode Available	0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attack	Aug 27, 2024	Adversarial AttackAutonomous Driving	CodeCode Available	0
Scaleable input gradient regularization for adversarial robustness	May 27, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability	Oct 14, 2020	Adversarial Attacktext-classification	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