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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 1201–1250 of 1746 papers

TitleStatusHype
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
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning—0
Partially Recentralization Softmax Loss for Vision-Language Models Robustness—0
SOAR: Second-Order Adversarial Regularization—0
Adversarial Robustness through Local Linearization—0
P-CapsNets: a General Form of Convolutional Neural Networks—0
An Empirical Evaluation of Adversarial Robustness under Transfer Learning—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Perception Over Time: Temporal Dynamics for Robust Image Understanding—0
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models—0
Adversarial Robustness Through Artifact Design—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
Adversarial Robustness: Softmax versus Openmax—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
Adversarial Robustness Overestimation and Instability in TRADES—0
Planting Undetectable Backdoors in Machine Learning Models—0
Playing it Safe: Adversarial Robustness with an Abstain Option—0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models—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
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism—0
Power up! Robust Graph Convolutional Network based on Graph Powering—0
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training—0
Adversarial Robustness on Image Classification with k-means—0
Adversarial Robustness of Visual Dialog—0
A case for new neural networks smoothness constraints—0
Adversarial Robustness of Streaming Algorithms through Importance Sampling—0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
Adversarial Purification with the Manifold Hypothesis—0
Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers—0
PRISON: Unmasking the Criminal Potential of Large Language Models—0
Adaptive Batch Normalization Networks for Adversarial Robustness—0
Adversarial robustness of sparse local Lipschitz predictors—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
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples—0
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches—0
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection—0
Proper 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