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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 251–300 of 1746 papers

TitleStatusHype
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?Code1
Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust AnalysisCode0
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-casesCode1
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustnessCode1
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Label Augmentation for Neural Networks Robustness—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter—0
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing CodingCode0
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection—0
Adversarial Robustification via Text-to-Image Diffusion ModelsCode1
Adversarially Robust Decision TransformerCode0
Scaling Trends in Language Model RobustnessCode0
Towards Robust Vision Transformer via Masked Adaptive Ensemble—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 ChallengeCode1
Relaxing Graph Transformers for Adversarial Attacks—0
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness—0
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques—0
PartImageNet++ Dataset: Scaling up Part-based Models for Robust RecognitionCode1
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays OffCode0
Deep Adversarial Defense Against Multilevel-Lp Attacks—0
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm AttacksCode1
How to beat a Bayesian adversary—0
Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias ElicitationCode0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness—0
Adversarial Robustness of VAEs across Intersectional SubgroupsCode0
L_p-norm Distortion-Efficient Adversarial Attack—0
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness—0
Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial RobustnessCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Towards unlocking the mystery of adversarial fragility of neural networks—0
DataFreeShield: Defending Adversarial Attacks without Training Data—0
Deciphering the Definition of Adversarial Robustness for post-hoc OOD DetectorsCode0
Understanding the Robustness of Graph Neural Networks against Adversarial AttacksCode0
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE—0
Dissecting Adversarial Robustness of Multimodal LM AgentsCode2
Adversaries With Incentives: A Strategic Alternative to Adversarial RobustnessCode0
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness—0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
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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