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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
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks	Sep 29, 2023	Adversarial AttackFace Swapping	CodeCode Available	1
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents	Sep 26, 2023	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Structure Invariant Transformation for better Adversarial Transferability	Sep 26, 2023	Adversarial AttackDiversity	CodeCode Available	1
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation	Sep 20, 2023	3D Human Pose EstimationAdversarial Attack	—Unverified	0
PRAT: PRofiling Adversarial aTtacks	Sep 20, 2023	Adversarial Attack	—Unverified	0
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
Semantic Adversarial Attacks via Diffusion Models	Sep 14, 2023	Adversarial Attack	CodeCode Available	1
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection	Sep 13, 2023	Adversarial AttackSpeech-to-Text	—Unverified	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