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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 101–150 of 1746 papers

TitleStatusHype
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement ErrorCode1
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
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives—0
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives—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
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Confidence Elicitation: A New Attack Vector for Large Language ModelsCode0
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 Reasoning at Jailbreaking TimeCode1
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language ModelsCode1
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees—0
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models—0
Boosting Adversarial Robustness and Generalization with Structural Prior—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 Robustness of Partitioned Quantum Classifiers—0
Adversarial Masked Autoencoder Purifier with Defense Transferability—0
Adversarially Robust Bloom Filters: Privacy, Reductions, and Open Problems—0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles—0
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—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
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN ArchitecturesCode1
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
On the Adversarial Robustness of Benjamini Hochberg—0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI—0
Rerouting LLM Routers—0
On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach—0
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF AttackCode0
Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game—0
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses—0
On Adversarial Robustness of Language Models in Transfer Learning—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—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