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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 1611–1620 of 1808 papers

Title	Date	Tasks	Status	Hype
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks	Jul 1, 2021	Adversarial Attack	CodeCode Available	0
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training	May 10, 2024	Adversarial AttackGraph Learning	CodeCode Available	0
Adversarial Self-Defense for Cycle-Consistent GANs	Aug 5, 2019	Adversarial AttackImage-to-Image Translation	CodeCode Available	0
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems	Nov 3, 2019	Adversarial AttackSpeaker Recognition	CodeCode Available	0
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification	Jul 8, 2023	Adversarial AttackPerson Re-Identification	CodeCode Available	0
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack	Jan 20, 2022	Adversarial AttackHard-label Attack	CodeCode Available	0
Learning Black-Box Attackers with Transferable Priors and Query Feedback	Oct 21, 2020	Adversarial Attack	CodeCode Available	0
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack	Jan 1, 2025	3D ReconstructionAdversarial Attack	CodeCode Available	0
BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks	Feb 6, 2025	Adversarial Attack	CodeCode Available	0
Deep k-NN Defense against Clean-label Data Poisoning Attacks	Sep 29, 2019	Adversarial AttackData Poisoning	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