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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 161–170 of 1808 papers

Title	Date	Tasks	Status	Hype
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity	Mar 10, 2022	Adversarial AttackSemantic Similarity	CodeCode Available	1
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon	Mar 8, 2022	Adversarial AttackTraffic Sign Recognition	CodeCode Available	1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking	Mar 3, 2022	Adversarial AttackObject Tracking	CodeCode Available	1
Random Walks for Adversarial Meshes	Feb 15, 2022	Adversarial AttackClassification	CodeCode Available	1
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark	Feb 14, 2022	Adversarial AttackScene Classification	CodeCode Available	1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios	Feb 10, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework	Feb 5, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?	Jan 31, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation	Jan 20, 2022	Adversarial AttackContrastive Learning	CodeCode Available	1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction	Jan 16, 2022	Adversarial AttackCombinatorial Optimization	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