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

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Showing 801–850 of 1808 papers

TitleStatusHype
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
ADMM based Distributed State Observer Design under Sparse Sensor Attacks—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—0
Boosting Adversarial Transferability through Enhanced Momentum—0
Boosting Adversarial Transferability of MLP-Mixer—0
Adversarial training with perturbation generator networks—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples—0
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Black-box Adversarial ML Attack on Modulation Classification—0
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
A Brief Survey on Deep Learning Based Data Hiding—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Adversarial Attack for Asynchronous Event-based Data—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving—0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Biologically inspired protection of deep networks from adversarial attacks—0
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
Adversarial Attacks against Deep Saliency Models—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Adversarial Relighting Against Face Recognition—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
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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