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Adversarial Defense

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Papers

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Showing 251–300 of 403 papers

TitleStatusHype
Modeling Adversarial Noise for Adversarial Defense—0
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view InconsistencyCode0
Modeling Adversarial Noise for Adversarial TrainingCode0
TREATED:Towards Universal Defense against Textual Adversarial Attacks—0
Neural Ensemble Search via Bayesian Sampling—0
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework—0
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Voting for the right answer: Adversarial defense for speaker verificationCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training—0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning—0
Adaptive Feature Alignment for Adversarial Training—0
Demotivate adversarial defense in remote sensing—0
Salient Feature Extractor for Adversarial Defense on Deep Neural NetworksCode0
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs—0
Evaluating Deception Detection Model Robustness To Linguistic Variation—0
Improved Autoregressive Modeling with Distribution Smoothing—0
Learning Defense Transformers for Counterattacking Adversarial ExamplesCode0
Internal Wasserstein Distance for Adversarial Attack and Defense—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Improving Hierarchical Adversarial Robustness of Deep Neural Networks—0
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids—0
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models—0
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning—0
A Comprehensive Evaluation Framework for Deep Model Robustness—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
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing—0
Exploiting Safe Spots in Neural Networks for Preemptive Robustness and Out-of-Distribution Detection—0
Target Training: Tricking Adversarial Attacks to Fail—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—0
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models—0
Layer-wise Adversarial Defense: An ODE Perspective—0
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment—0
Defending against black-box adversarial attacks with gradient-free trained sign activation neural networksCode0
SAD: Saliency Adversarial Defense without Adversarial Training—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Learning Energy-Based Models With Adversarial TrainingCode0
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation—0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images—0
Self-Gradient Networks—0
Adversarial defense for deep speaker recognition using hybrid adversarial training—0
WaveTransform: Crafting Adversarial Examples via Input Decomposition—0
Attack Agnostic Adversarial Defense via Visual Imperceptible Bound—0
An Analysis of Robustness of Non-Lipschitz NetworksCode0
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated GradientsCode0
Benchmarking adversarial attacks and defenses for time-series data—0
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses—0
Manifold Projection for Adversarial Defense on Face Recognition—0
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All datasetsCIFAR-10ImageNet (non-targeted PGD, max perturbation=4)ImageNetImageNet (targeted PGD, max perturbation=16)MNISTCAAD 2018miniImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WRN-28-10Accuracy90.03—Unverified
2Diffusion ClassifierAccuracy89.85—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-10Accuracy84.3—Unverified
4Ours (Stochastic-LWTA/PGD/WideResNet-34-5)Accuracy83.4—Unverified
5Ours (Stochastic-LWTA/PGD/WideResNet-34-1)Accuracy81.87—Unverified
6ResNet18 (TRADES-ANCRA/PGD-40)Accuracy81.7—Unverified
7Stochastic-LWTA/PGD/WideResNet-34-5Attack: AutoAttack81.22—Unverified
8PCL (against PGD, white box)Accuracy46.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SAT-EfficientNet-L1Accuracy58.6—Unverified
2LLR-ResNet-152Accuracy47—Unverified
3ResNet-152 free-m=4Accuracy36—Unverified
4ResNet-101 free-m=4Accuracy34.3—Unverified
5ResNet-50 free-m=4Accuracy31.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet101Accuracy99.8—Unverified
2InceptionV3Accuracy98.6—Unverified
3Feature DenoisingAccuracy49.5—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152 DenoiseAccuracy42.8—Unverified
2ResNeXt-101 DenoiseAllAccuracy40.4—Unverified
3ResNet-152Accuracy39—Unverified
#ModelMetricClaimedVerifiedStatus
1Defense GANAccuracy0.85—Unverified
2PuVAEAccuracy0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1Feature DenoisingAccuracy50.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Auto Encoder-Block Switching defense with GradCAMAccuracy 88.54—Unverified