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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 11–20 of 1808 papers

Title	Date	Tasks	Status	Hype
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models	Mar 4, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	2
Fast Adversarial Attacks on Language Models In One GPU Minute	Feb 23, 2024	Adversarial AttackComputational Efficiency	CodeCode Available	2
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks	Jan 27, 2024	Adversarial AttackComputational Efficiency	CodeCode Available	2
Diffusion Models for Imperceptible and Transferable Adversarial Attack	May 14, 2023	Adversarial Attack	CodeCode Available	2
Ignore Previous Prompt: Attack Techniques For Language Models	Nov 17, 2022	Adversarial AttackAdversarial Text	CodeCode Available	2
Efficient Neural Network Analysis with Sum-of-Infeasibilities	Mar 19, 2022	Adversarial AttackEfficient Neural Network	CodeCode Available	2
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints	Feb 25, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	2
Attacking and Defending Machine Learning Applications of Public Cloud	Jul 27, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	2
Backdoor Learning: A Survey	Jul 17, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	2
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP	Apr 29, 2020	Adversarial AttackAdversarial Text	CodeCode Available	2

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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