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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 1001–1050 of 1746 papers

TitleStatusHype
Topological Signatures of Adversaries in Multimodal Alignments—0
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses—0
Generalized No Free Lunch Theorem for Adversarial Robustness—0
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG—0
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks—0
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Long-tailed Adversarial Training with Self-Distillation—0
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation—0
An empirical study of pretrained representations for few-shot classification—0
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks—0
LOTS about Attacking Deep Features—0
Adversarial Attacks and Defenses for Speech Recognition Systems—0
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training—0
L_p-norm Distortion-Efficient Adversarial Attack—0
Towards Reliable Neural Specifications—0
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness—0
Lyapunov Neural ODE State-Feedback Control Policies—0
Maintaining Adversarial Robustness in Continuous Learning—0
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering—0
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers—0
And/or trade-off in artificial neurons: impact on adversarial robustness—0
Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification—0
Universal Adversarial Training with Class-Wise Perturbations—0
Measuring Adversarial Datasets—0
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition—0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective—0
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks—0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits—0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives—0
Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers—0
Min-Max Optimization under Delays—0
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition—0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness—0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness—0
Towards Adversarially Robust Continual Learning—0
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off—0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations—0
Universally Robust Graph Neural Networks by Preserving Neighbor Similarity—0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles—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