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 1301–1350 of 1808 papers

TitleStatusHype
Universal Adversarial Perturbations and Image Spam Classifiers—0
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial AttackCode0
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models—0
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial TrainingCode0
A Brief Survey on Deep Learning Based Data Hiding—0
Model-Agnostic Defense for Lane Detection against Adversarial AttackCode0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Certifiably Robust Variational Autoencoders—0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target ScenesCode0
RoBIC: A benchmark suite for assessing classifiers robustnessCode0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models—0
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems—0
Towards Practical Robustness Analysis for DNNs based on PAC-Model LearningCode0
Generating Black-Box Adversarial Examples in Sparse Domain—0
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack—0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization—0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Random Transformation of Image Brightness for Adversarial AttackCode0
Exploring Adversarial Fake Images on Face Manifold—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
Robust Text CAPTCHAs Using Adversarial Examples—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Towards Robustness of Deep Neural Networks via Regularization—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Stabilized Medical Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Practical Order Attack in Deep Ranking—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Adversarial Example Detection Using Latent Neighborhood Graph—0
An Adversarial Attack via Feature Contributive Regions—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis—0
A Hierarchical Feature Constraint to Camouflage Medical Adversarial AttacksCode0
Show:102550
← PrevPage 27 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