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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 681–690 of 1808 papers

Title	Date	Tasks	Status	Hype
Left-right Discrepancy for Adversarial Attack on Stereo Networks	Jan 14, 2024	Adversarial AttackDisparity Estimation	—Unverified	0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability	Jan 13, 2024	Adversarial Attackimage-classification	—Unverified	0
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Transferable Learned Image Compression-Resistant Adversarial Perturbations	Jan 6, 2024	Adversarial AttackAutonomous Driving	—Unverified	0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging	Jan 4, 2024	Adversarial AttackDomain Adaptation	CodeCode Available	0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing	Jan 2, 2024	Adversarial AttackFace Anti-Spoofing	—Unverified	0
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles	Dec 31, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation	Dec 30, 2023	Adversarial AttackClassification	—Unverified	0
Towards adversarial robustness verification of no-reference image-and video-quality metrics	Dec 30, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation	Dec 28, 2023	Adversarial AttackNode Classification	—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