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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 211–220 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection	May 27, 2023	Adversarial Attackobject-detection	CodeCode Available	1
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model	Aug 17, 2024	Adversarial AttackSpeaker Verification	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
MENLI: Robust Evaluation Metrics from Natural Language Inference	Aug 15, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection	Jul 10, 2020	Adversarial Attack	CodeCode Available	1
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior	Mar 17, 2020	Adversarial AttackVideo Classification	CodeCode Available	1
Multi-attacks: Many images + the same adversarial attack many target labels	Aug 4, 2023	Adversarial Attack	CodeCode Available	1
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape	Apr 24, 2024	Adversarial AttackFace Swapping	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