Selected article for: "accuracy achieve and model accuracy achieve"
Author: Gupta, Laveesh; Gupta, Muskan; Meeradevi; Khaitan, Nishit; Mundada, Monica R.
Title: Digital Watermarking to Protect Deep Learning Model
  • Cord-id: up9338q4
  • Document date: 2021_3_13
    • ID: up9338q4
    Snippet: There has been a significant progress in deep neural network. It is necessary to protect one’s model to prove his/her ownership. This can be achieved by embedding meaningful content or some irrelevant data or noise in the training data as watermark to protect deep neural network. In this paper, we embedded ‘WM’ character as a watermark to training images. To protect the rights of the shared trained models, we propose digital watermarking in this paper. The model was trained with both coron
    Document: There has been a significant progress in deep neural network. It is necessary to protect one’s model to prove his/her ownership. This can be achieved by embedding meaningful content or some irrelevant data or noise in the training data as watermark to protect deep neural network. In this paper, we embedded ‘WM’ character as a watermark to training images. To protect the rights of the shared trained models, we propose digital watermarking in this paper. The model was trained with both corona virus disease-19 (COVID-19) infected and non-infected peoples’ chest X-rays with a total of 2000 images. The model could achieve accuracy above 96%.

    Search related documents:
    Co phrase search for related documents
    • absolute error and accurate prediction: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
    • absolute error and activation function: 1, 2
    • absolute error and machine learn: 1
    • accuracy affect and activation function: 1