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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 681–690 of 1808 papers

Title	Date	Tasks	Status	Hype
Boosting Adversarial Transferability via Gradient Relevance Attack	Jan 1, 2023	Adversarial Attack	CodeCode Available	1
Frequency-aware GAN for Adversarial Manipulation Generation	Jan 1, 2023	Adversarial AttackDecoder	—Unverified	0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients	Jan 1, 2023	Adversarial Attack	—Unverified	0
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions	Jan 1, 2023	Adversarial Attack	—Unverified	0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation	Jan 1, 2023	Adversarial Attack	—Unverified	0
Proximal Splitting Adversarial Attack for Semantic Segmentation	Jan 1, 2023	Adversarial AttackSegmentation	CodeCode Available	1
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection	Jan 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing	Jan 1, 2023	Adversarial AttackData Augmentation	—Unverified	0
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts	Jan 1, 2023	Adversarial AttackAdversarial Text	CodeCode Available	1
Angelic Patches for Improving Third-Party Object Detector Performance	Jan 1, 2023	Adversarial AttackObject	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