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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 1271–1280 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting	Apr 22, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors	Apr 22, 2021	Adversarial AttackObject	CodeCode Available	0
Robust Certification for Laplace Learning on Geometric Graphs	Apr 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions	Apr 22, 2021	Adversarial Attack	—Unverified	0
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models	Apr 19, 2021	Adversarial AttackManagement	CodeCode Available	0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Apr 18, 2021	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	0
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune	Apr 13, 2021	Adversarial Attack	—Unverified	0
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing	Apr 10, 2021	Adversarial Attack	—Unverified	0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network	Apr 7, 2021	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0

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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