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

Showing 926950 of 1808 papers

TitleStatusHype
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack0
Random Walks for Adversarial MeshesCode1
Universal Adversarial Examples in Remote Sensing: Methodology and BenchmarkCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Adversarial Attack and Defense for Non-Parametric Two-Sample TestsCode0
Attacking c-MARL More Effectively: A Data Driven Approach0
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) frameworkCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning0
Robust Unpaired Single Image Super-Resolution of Faces0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective0
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges0
Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and AdverbsCode0
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial AttackCode0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Phrase-level Textual Adversarial Attack with Label Preservation0
Residue-Based Natural Language Adversarial Attack Detection0
SSCAE: A Novel Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator0
ALA: Naturalness-aware Adversarial Lightness Attack0
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified