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 1301–1325 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
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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