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DeepMutation: Mutation Testing of Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm where ...
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Multimodal Deep Learning for Flaw Detection in Software Programs
We explore the use of multiple deep learning models for detecting flaws ...
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Tamil Vowel Recognition With Augmented MNIST-like Data Set
We report generation of a MNIST [4] compatible data set [1] for Tamil vo...
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Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training ...
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Coverage Testing of Deep Learning Models using Dataset Characterization
Deep Neural Networks (DNNs), with its promising performance, are being i...
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Scaling down Deep Learning
Though deep learning models have taken on commercial and political relev...
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MORF: A Framework for MOOC Predictive Modeling and Replication At Scale
The MOOC Replication Framework (MORF) is a novel software system for fea...
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The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
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) models with trojans at scale. We utilize the developed framework to generate a large set of trojaned MNIST classifiers, as well as demonstrate the capability to produce a trojaned reinforcement-learning model using vector observations. Results on MNIST show that the nature of the trigger, training batch size, and dataset poisoning percentage all affect successful embedding of trojans. We test Neural Cleanse against the trojaned MNIST models and successfully detect anomalies in the trained models approximately 18% of the time. Our experiments and workflow indicate that the TrojAI software framework will enable researchers to easily understand the effects of various configurations of the dataset and training hyperparameters on the generated trojaned deep learning model, and can be used to rapidly and comprehensively test new trojan detection methods.
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