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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 321–330 of 1808 papers

Title	Date	Tasks	Status	Hype
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection	May 16, 2024	Adversarial AttackFace Recognition	CodeCode Available	2
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models	May 16, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer	May 15, 2024	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training	May 10, 2024	Adversarial AttackGraph Learning	CodeCode Available	0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing	May 10, 2024	Adversarial Attack	—Unverified	0
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models	May 9, 2024	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	1
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization	May 9, 2024	Adversarial AttackDeep Learning	—Unverified	0
Universal Adversarial Perturbations for Vision-Language Pre-trained Models	May 9, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Untargeted Adversarial Attack on Knowledge Graph Embeddings	May 8, 2024	Adversarial AttackGraph Embedding	—Unverified	0
Revisiting Character-level Adversarial Attacks for Language Models	May 7, 2024	Adversarial AttackSentence	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