47 citations · 172 across the 92 of their papers we have counts for
7 papers · 1 filter
Out-of-Distribution Detection with Overlap Index
Hao Fu, Prashanth Krishnamurthy, Siddharth Garg +1
Out-of-distribution (OOD) detection is crucial for the deployment of machine learning models in the open world. While existing OOD detectors are effective in identifying OOD sample…
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Patricia Pauli, Aaron Havens, Alexandre Araujo +4
Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab…
Privacy-Preserving Collaborative Learning through Feature Extraction
Alireza Sarmadi, Hao Fu, Prashanth Krishnamurthy +2
We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings fro…
An Upper Bound for the Distribution Overlap Index and Its Applications
Hao Fu, Prashanth Krishnamurthy, Siddharth Garg +1
This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The compu…
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems
Naman Patel, Prashanth Krishnamurthy, Siddharth Garg +1
We show that by controlling parts of a physical environment in which a pre-trained deep neural network (DNN) is being fine-tuned online, an adversary can launch subtle data poisoni…
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
Hao Fu, Akshaj Kumar Veldanda, Prashanth Krishnamurthy +2
This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers. Our defense is…
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
Naman Patel, Apoorva Nandini Saridena, Anna Choromanska +2
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle…