SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1476–1500 of 1808 papers

TitleStatusHype
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
Universalization of any adversarial attack using very few test examplesCode0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier—0
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability—0
Minority Reports Defense: Defending Against Adversarial Patches—0
Transferable Perturbations of Deep Feature Distributions—0
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model—0
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack—0
Improved Adversarial Training via Learned Optimizer—0
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty—0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
Towards Transferable Adversarial Attack against Deep Face Recognition—0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images—0
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen CamerasCode0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified