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 1451–1500 of 1808 papers

TitleStatusHype
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Generating Adversarial Examples with an Optimized Quality—0
Adversarial Attacks for Multi-view Deep Models—0
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust PredictionsCode0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
Global Robustness Verification Networks—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
ILFO: Adversarial Attack on Adaptive Neural Networks—0
Modeling Biological Immunity to Adversarial Examples—0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
Polishing Decision-Based Adversarial Noise With a Customized Sampling—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
Evaluations and Methods for Explanation through Robustness Analysis—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning—0
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
Show:102550
← PrevPage 30 of 37Next →
All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified