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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 751–760 of 1808 papers

Title	Date	Tasks	Status	Hype
Model Leeching: An Extraction Attack Targeting LLMs	Sep 19, 2023	Adversarial Attackmodel	—Unverified	0
Transferable Adversarial Attack on Image Tampering Localization	Sep 19, 2023	Adversarial Attack	—Unverified	0
Context-aware Adversarial Attack on Named Entity Recognition	Sep 16, 2023	Adversarial Attacknamed-entity-recognition	—Unverified	0
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection	Sep 13, 2023	Adversarial AttackSpeech-to-Text	—Unverified	0
Outlier Robust Adversarial Training	Sep 10, 2023	Adversarial AttackBinary Classification	CodeCode Available	0
Adaptive Adversarial Training Does Not Increase Recourse Costs	Sep 5, 2023	Adversarial Attack	—Unverified	0
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration	Sep 4, 2023	Adversarial AttackFace Recognition	—Unverified	0
MathAttack: Attacking Large Language Models Towards Math Solving Ability	Sep 4, 2023	Adversarial AttackGSM8K	—Unverified	0
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models	Sep 2, 2023	Adversarial Attackregression	—Unverified	0
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning	Aug 31, 2023	Adversarial Attack	CodeCode Available	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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