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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 521–530 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attack and Defense on Graph Data: A Survey	Dec 26, 2018	Adversarial Attackimage-classification	CodeCode Available	0	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing	May 24, 2023	Adversarial Attack	CodeCode Available	0	5
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models	Apr 19, 2021	Adversarial AttackManagement	CodeCode Available	0	5
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization	Sep 23, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0	5
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image	Apr 27, 2025	Adversarial Attack	CodeCode Available	0	5
Physics-constrained Attack against Convolution-based Human Motion Prediction	Jun 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5

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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