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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

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Showing 1551–1600 of 1746 papers

TitleStatusHype
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime—0
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators—0
Calibration and Consistency of Adversarial Surrogate Losses—0
Buffer Zone based Defense against Adversarial Examples in Image Classification—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Eight challenges in developing theory of intelligence—0
Bridged Adversarial Training—0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach—0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Training Graph Neural Networks Using Non-Robust Samples—0
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble—0
Semantics-Preserving Adversarial Training—0
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness—0
Enhancing Adversarial Robustness in SNNs with Sparse Gradients—0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization—0
Boosting Adversarial Robustness and Generalization with Structural Prior—0
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness—0
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction—0
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation—0
Sequential Bayesian Neural Subnetwork Ensembles—0
Enhancing Quantum Adversarial Robustness by Randomized Encodings—0
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks—0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations—0
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models—0
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks—0
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness—0
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners—0
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification—0
Estimating the Adversarial Robustness of Attributions in Text with Transformers—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism—0
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks—0
Evaluating adversarial robustness in simulated cerebellum—0
Evaluating Adversarial Robustness in the Spatial Frequency Domain—0
Shortcut Learning of Large Language Models in Natural Language Understanding—0
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks—0
Evaluating Adversarial Robustness on Document Image Classification—0
Evaluating Adversarial Robustness with Expected Viable Performance—0
Adversarially Robust Estimate and Risk Analysis in Linear Regression—0
Evaluating robustness of support vector machines with the Lagrangian dual approach—0
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified