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Found 5 papers, 0 papers with code
NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness
This tool was deployed and used in a Hackathon event to evaluate the reliability of a skin cancer image detector.
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
In recent years, Deep Neural Network models have been developed in different fields, where they have brought many advances.
Topological safeguard for evasion attack interpreting the neural networks' behavior
In particular, the widely known evasion attack is being analyzed by researchers; thus, several defenses to avoid such a threat can be found in the literature.
NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices
In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task.
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment
The idea was developed using a breast cancer dataset and a VGG16 and dense neural network model, but the solutions could be applied to datasets from other areas and different convolutional and dense deep neural network models.
