20 citations · 47 across the 19 of their papers we have counts for
8 papers · 1 filter
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers
Chace Ashcraft, Ted Staley, Josh Carney +4
Backdoor attacks, or trojans, pose a security risk by concealing undesirable behavior in deep neural network models. Open-source neural networks are downloaded from the internet da…
Investigating the Treacherous Turn in Deep Reinforcement Learning
Chace Ashcraft, Kiran Karra, Josh Carney +1
The Treacherous Turn refers to the scenario where an artificial intelligence (AI) agent subtly, and perhaps covertly, learns to perform a behavior that benefits itself but is deeme…
Machine Learning aided Crop Yield Optimization
Chace Ashcraft, Kiran Karra
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL…
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks
Kiran Karra, Chace Ashcraft, Cash Costello
Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn genera…
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
Chace Ashcraft, Kiran Karra
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are tri…
The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
Kiran Karra, Chace Ashcraft, Neil Fendley
In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL)…
Learning Approximate Neural Estimators for Wireless Channel State Information
Timothy J. O'Shea, Kiran Karra, T. Charles Clancy
Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical…
Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
Timothy J O'Shea, Kiran Karra, T. Charles Clancy
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel. We treat the problem as reconstruction optimization throu…