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 1351–1375 of 1808 papers

TitleStatusHype
Exacerbating Algorithmic Bias through Fairness AttacksCode0
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm OptimizationCode0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Towards Natural Robustness Against Adversarial Examples—0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers—0
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation TechniquesCode0
Adversarial Attacks on Deep Graph Matching—0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack—0
Enhancing Neural Models with Vulnerability via Adversarial AttackCode0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack—0
A Targeted Universal Attack on Graph Convolutional NetworkCode0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images—0
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors—0
Adversarial Attack on Facial Recognition using Visible Light—0
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization—0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks—0
Multi-Task Adversarial Attack—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Dynamic backdoor attacks against federated learning—0
Fooling the primate brain with minimal, targeted image manipulation—0
Efficient and Transferable Adversarial Examples from Bayesian Neural NetworksCode0
Bridging the Performance Gap between FGSM and PGD Adversarial TrainingCode0
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation DifficultyCode0
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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