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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 491–500 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack	Jan 18, 2024	Adversarial AttackStyle Transfer	CodeCode Available	0	5
Adversarial attacks on neural networks through canonical Riemannian foliations	Mar 2, 2022	Adversarial Attack	CodeCode Available	0	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0	5
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection	Nov 8, 2023	Active LearningAdversarial Attack	CodeCode Available	0	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	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
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack	May 21, 2023	Adversarial Attack	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	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