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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 1051–1100 of 1746 papers

TitleStatusHype
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness—0
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks—0
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks—0
The Effects of Image Distribution and Task on Adversarial Robustness—0
The Importance of Robust Features in Mitigating Catastrophic Forgetting—0
The Intrinsic Dimension of Images and Its Impact on Learning—0
The Lipschitz Constant of Self-Attention—0
The Many Faces of Adversarial Risk—0
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
The Pros and Cons of Adversarial Robustness—0
The robust way to stack and bag: the local Lipschitz way—0
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness—0
The Sword of Damocles in ViTs: Computational Redundancy Amplifies Adversarial Transferability—0
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search—0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
Tools and Practices for Responsible AI Engineering—0
Topological Signatures of Adversaries in Multimodal Alignments—0
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks—0
Towards Reliable Neural Specifications—0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits—0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
Towards Adversarially Robust Continual Learning—0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles—0
Towards Adversarially Robust Deep Image Denoising—0
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples—0
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques—0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Towards Adversarial Robustness of Deep Vision Algorithms—0
Towards Adversarial Robustness via Transductive Learning—0
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment—0
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness—0
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation—0
Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Towards Certifiable Adversarial Sample Detection—0
Towards Compact and Robust Deep Neural Networks—0
Towards Defending against Adversarial Examples via Attack-Invariant Features—0
Towards Disentangling Non-Robust and Robust Components in Performance Metric—0
Towards Efficient Formal Verification of Spiking Neural Network—0
An Empirical Evaluation of Adversarial Robustness under Transfer Learning—0
Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Towards Proving the Adversarial Robustness of Deep Neural Networks—0
Towards quantum enhanced adversarial robustness in machine learning—0
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness—0
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI—0
Towards Robust and Accurate Visual Prompting—0
Towards Robust Deep Neural Networks—0
Towards Robust Graph Contrastive Learning—0
Towards Robust Image Classification Using Sequential Attention Models—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