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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 241–250 of 1808 papers

Title	Date	Tasks	Status	Hype
Random Walks for Adversarial Meshes	Feb 15, 2022	Adversarial AttackClassification	CodeCode Available	1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?	Jan 31, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	May 7, 2021	Adversarial AttackFace Generation	CodeCode Available	1
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks	May 24, 2022	Adversarial AttackGraph Neural Network	CodeCode Available	1
Rethinking Image Restoration for Object Detection	Nov 1, 2022	Adversarial AttackDomain Adaptation	CodeCode Available	1
Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks	Jan 16, 2024	Adversarial AttackImage Generation	CodeCode Available	1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1
Robust Mid-Pass Filtering Graph Convolutional Networks	Feb 16, 2023	Adversarial AttackNode Classification	CodeCode Available	1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps	Jan 12, 2021	Adversarial AttackDeep Learning	CodeCode Available	1
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1

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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