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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 851–900 of 1746 papers

TitleStatusHype
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering—0
On the (Un-)Avoidability of Adversarial Examples—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains—0
On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach—0
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey—0
Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification—0
Optimising Neural Network Architectures for Provable Adversarial Robustness—0
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness—0
Optimization and Optimizers for Adversarial Robustness—0
Optimized Potential Initialization for Low-latency Spiking Neural Networks—0
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach—0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
Output Perturbation for Differentially Private Convex Optimization: Faster and More General—0
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis—0
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization—0
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks—0
Parameterizing Activation Functions for Adversarial Robustness—0
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness—0
Partially Recentralization Softmax Loss for Vision-Language Models Robustness—0
P-CapsNets: a General Form of Convolutional Neural Networks—0
Perception Over Time: Temporal Dynamics for Robust Image Understanding—0
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Perlin Noise Improve Adversarial Robustness—0
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off—0
Perturbation Type Categorization for Multiple _p Bounded Adversarial Robustness—0
Phase-shifted Adversarial Training—0
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications—0
Visually Adversarial Attacks and Defenses in the Physical World: A Survey—0
Planting Undetectable Backdoors in Machine Learning Models—0
Playing it Safe: Adversarial Robustness with an Abstain Option—0
Poisoning Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks—0
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
Policy Smoothing for Provably Robust Reinforcement Learning—0
Power up! Robust Graph Convolutional Network based on Graph Powering—0
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training—0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers—0
PRISON: Unmasking the Criminal Potential of Large Language Models—0
Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide—0
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach—0
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches—0
Proper Measure for Adversarial Robustness—0
Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility—0
Pro-tuning: Unified Prompt Tuning for Vision Tasks—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—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