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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 951–1000 of 1746 papers

TitleStatusHype
Invariance vs Robustness of Neural Networks—0
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications—0
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Investigating the Impact of Quantization on Adversarial Robustness—0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
A Novel Noise Injection-based Training Scheme for Better Model Robustness—0
Adversarial Bone Length Attack on Action Recognition—0
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?—0
Is current research on adversarial robustness addressing the right problem?—0
A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles—0
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models—0
A Non-monotonic Smooth Activation Function—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness—0
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks—0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks—0
The Pros and Cons of Adversarial Robustness—0
Kernels, Data & Physics—0
The robust way to stack and bag: the local Lipschitz way—0
Achieving Adversarial Robustness via Sparsity—0
k-Mixup Regularization for Deep Learning via Optimal Transport—0
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs—0
Knowledge-guided Semantic Computing Network—0
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness—0
Label Smoothing and Adversarial Robustness—0
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?—0
The Sword of Damocles in ViTs: Computational Redundancy Amplifies Adversarial Transferability—0
Achieving Adversarial Robustness Requires An Active Teacher—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
An Experimental Study of Semantic Continuity for Deep Learning Models—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms—0
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search—0
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck—0
An Ensemble Approach Towards Adversarial Robustness—0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection—0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
Adversarial Attacks on Hyperbolic Networks—0
Lyapunov-Stable Deep Equilibrium Models—0
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness—0
Learning Sample Reweighting for Accuracy and Adversarial Robustness—0
Learning Sample Reweighting for Adversarial Robustness—0
Tools and Practices for Responsible AI Engineering—0
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks—0
Lessons from Defending Gemini Against Indirect Prompt Injections—0
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
Life-Cycle Routing Vulnerabilities of LLM Router—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