Native Robustness

Native robustness in machine learning focuses on developing models inherently resistant to various forms of input perturbations, including adversarial attacks and noisy data, without relying solely on post-hoc defenses. Current research emphasizes techniques like ensemble methods, reprogramming existing models, and modifying training procedures (e.g., using different learning rates for specific model layers or incorporating regularization methods) to improve robustness across diverse model architectures, including convolutional neural networks, vision transformers, and large language models. This field is crucial for deploying reliable AI systems in safety-critical applications, such as healthcare and autonomous driving, where model resilience to unexpected inputs is paramount.

Papers

September 4, 2024

Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble
Chang Dong, Zhengyang Li, Liangwei Zheng, Weitong Chen, Wei Emma Zhang
Native Robustness Adversarial Robustness Time Series Classification Randomized Smoothing Robustness Certificate Robustness Radius Self Ensemble \Ell_p$ Robustness

September 3, 2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Joel Brogan, Olivera Kotevska, Anibely Torres, Sumit Jha, Mark Adams
Native Robustness Deep Learning Model Signal Processing Spectrogram Based Signal Classification Neural Stochastic Differential Equation Signal Identification

September 2, 2024

September 1, 2024

Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Ali Alfatemi, Nasir Ghani, Vu Khanh Quy, Abdellah Chehri
Native Robustness Federated Learning Inherent Interpretability Industrial Internet of Thing Industrial Internet

August 29, 2024

PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning
Noor Hussein, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Native Robustness Vision Language Model Prompt Learning Medical Image Analysis Image Text Prompt Space

August 27, 2024

August 26, 2024

August 25, 2024

August 24, 2024

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Jiwei Guan, Tianyu Ding, Longbing Cao, Lei Pan, Chen Wang, Xi Zheng
Adversarial Attack Native Robustness Adversarial Robustness Vision Language Adversarial Perturbation Adversarial Vulnerability Multimodal Attack

August 23, 2024

Deep Learning at the Intersection: Certified Robustness as a Tool for 3D Vision
Gabriel Pérez S, Juan C. Pérez, Motasem Alfarra, Jesús Zarzar, Sara Rojas, Bernard Ghanem, Pablo Arbeláez
Deep Learning Native Robustness Intersection Scenario BRIO Tool Randomized Smoothing Signed Distance Function Voxel Grid Robustness Radius

August 22, 2024

Assessing the Uncertainty and Robustness of Object Detection Models for Detecting Stickers on Laptops
Chengjie Lu, Jiahui Wu, Shaukat Ali, Mikkel Labori Olsen
Native Robustness Uncertainty Quantification High Uncertainty Anticipation Object Detection Model Object Surface Image Datasets Digital Computer

August 21, 2024

August 20, 2024

Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information
Ming Jiang, Tingting Huang, Biao Guo, Yao Lu, Feng Zhang
Large Language Model Native Robustness Global Impact Reasoning Capability Complex Reasoning Task Problem Solving Reasoning Performance Irrelevant Information

August 19, 2024

The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
Niyar R Barman, Krish Sharma, Ashhar Aziz, Shashwat Bajpai, Shwetangshu Biswas, Vasu Sharma, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
Native Robustness Paraphrase Generation Visual Text Brittle Fracture Deep Watermarking Paraphrase Dataset Paraphrasing Attack

Native Robustness

Papers

Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness

Bootstrap SGD: Algorithmic Stability and Robustness

Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness

PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns

MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues

GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets

On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective

Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach

Deep Learning at the Intersection: Certified Robustness as a Tool for 3D Vision

Assessing the Uncertainty and Robustness of Object Detection Models for Detecting Stickers on Laptops

Exploring Robustness of Visual State Space model against Backdoor Attacks

The Vizier Gaussian Process Bandit Algorithm

Deep Reinforcement Learning for Decentralized Multi-Robot Control: A DQN Approach to Robustness and Information Integration

Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information

The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks