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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 151–200 of 1746 papers

TitleStatusHype
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness—0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
Evaluating the Adversarial Robustness of Detection Transformers—0
On the Local Complexity of Linear Regions in Deep ReLU Networks—0
Efficient Contrastive Explanations on DemandCode0
Adversarial Robustness through Dynamic Ensemble Learning—0
Holistic Adversarially Robust Pruning—0
Training Graph Neural Networks Using Non-Robust Samples—0
Targeted View-Invariant Adversarial Perturbations for 3D Object RecognitionCode0
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan ScriptCode1
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic SegmentationCode0
Learning Robust and Privacy-Preserving Representations via Information TheoryCode0
Improving Graph Neural Networks via Adversarial Robustness Evaluation—0
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language ModelsCode0
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks—0
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-MemorizationCode0
Understanding the Impact of Graph Reduction on Adversarial Robustness in Graph Neural Networks—0
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual SimilarityCode0
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative ModelsCode1
Adversarial Attacks on Hyperbolic Networks—0
Adversarial Prompt Distillation for Vision-Language Models—0
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles—0
WARLearn: Weather-Adaptive Representation LearningCode0
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual AttacksCode0
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models—0
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods—0
Edge-Only Universal Adversarial Attacks in Distributed Learning—0
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time—0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified RobustnessCode0
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model GeneralizationCode0
Rapid Response: Mitigating LLM Jailbreaks with a Few Examples—0
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems—0
Adversarial Robustness of In-Context Learning in Transformers for Linear Regression—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Neural Fingerprints for Adversarial Attack DetectionCode0
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training—0
CausAdv: A Causal-based Framework for Detecting Adversarial ExamplesCode0
Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language ModelsCode1
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—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