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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 401–450 of 1746 papers

TitleStatusHype
Survey of Adversarial Robustness in Multimodal Large Language Models—0
Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language ModelsCode0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Robustness Tokens: Towards Adversarial Robustness of TransformersCode0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation MethodsCode0
MMARD: Improving the Min-Max Optimization Process in Adversarial Robustness Distillation—0
Long-tailed Adversarial Training with Self-Distillation—0
Life-Cycle Routing Vulnerabilities of LLM Router—0
Exploring Adversarial Transferability between Kolmogorov-arnold Networks—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Transformer Meets Twicing: Harnessing Unattended Residual InformationCode0
Adversarial Robustness in Parameter-Space Classifiers—0
Evaluation of Hate Speech Detection Using Large Language Models and Geographical ContextualizationCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear InterpolationCode0
Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide—0
Generalization Certificates for Adversarially Robust Bayesian Linear Regression—0
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives—0
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives—0
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms—0
General Coded Computing: Adversarial Settings—0
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
A Survey on Explainable Deep Reinforcement Learning—0
Confidence Elicitation: A New Attack Vector for Large Language ModelsCode0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting—0
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach—0
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks—0
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees—0
Boosting Adversarial Robustness and Generalization with Structural Prior—0
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models—0
Trading Inference-Time Compute for Adversarial Robustness—0
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius MaximizationCode0
Topological Signatures of Adversaries in Multimodal Alignments—0
Adversarial Masked Autoencoder Purifier with Defense Transferability—0
Adversarial Robustness of Partitioned Quantum Classifiers—0
Adversarially Robust Bloom Filters: Privacy, Reductions, and Open Problems—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis—0
A margin-based replacement for cross-entropy loss—0
With Great Backbones Comes Great Adversarial Transferability—0
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution—0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
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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