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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
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness—0
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness—0
Impact of Attention on Adversarial Robustness of Image Classification Models—0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Impact of Spatial Frequency Based Constraints on Adversarial Robustness—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
Adversarial Examples in Environment Perception for Automated Driving (Review)—0
Improve Adversarial Robustness via Weight Penalization on Classification Layer—0
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations—0
Improved Adversarial Robustness via Logit Regularization Methods—0
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams—0
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition—0
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks—0
Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking—0
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum—0
Robust Adversarial Classification via Abstaining—0
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models—0
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations—0
Assessing Adversarial Robustness of Large Language Models: An Empirical Study—0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models—0
Improving Adversarial Robustness by Encouraging Discriminative Features—0
Improving Adversarial Robustness by Contrastive Guided Diffusion Process—0
A Spectral Perspective towards Understanding and Improving Adversarial Robustness—0
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training—0
Improving Adversarial Robustness for Free with Snapshot Ensemble—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Improving Adversarial Robustness in Weight-quantized Neural Networks—0
Improving adversarial robustness of deep neural networks by using semantic information—0
Test-Time Adaptation and Adversarial Robustness—0
Improving Adversarial Robustness of Ensembles with Diversity Training—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning—0
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing—0
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—0
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Test-Time Adaptation with Perturbation Consistency Learning—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Improving Adversarial Robustness via Feature Pattern Consistency Constraint—0
Adversarial Examples are Misaligned in Diffusion Model Manifolds—0
Adversarial Examples Are a Natural Consequence of Test Error in Noise—0
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?—0
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting—0
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations—0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction—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