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Connecting the dots: Toward accountable machine-learning printer attribution methods

  • Published:2018-05 Issue: Volume:53 Page:257-272
  • ISSN:1047-3203
  • Container-title:Journal of Visual Communication and Image Representation
  • language:en
  • Short-container-title:Journal of Visual Communication and Image Representation

Author:

Navarro Luiz C.,Navarro Alexandre K.W.,Rocha Anderson,Dahab Ricardo

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Media Technology,Signal Processing

Reference51 articles.

1. Vision of the unseen: current trends and challenges in digital image and video forensics;Rocha;ACM Comput. Surv.,2011

2. Detecting predatory conversations in social media by deep Convolutional Neural Networks;Ebrahimi;Dig. Invest.,2016

3. A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering;Uçar;Neural Comput. Appl.,2016

4. Laser printer attribution: exploring new features and beyond;Ferreira;Forensic Sci. Int.,2016

5. T. Gal, J. Sandor, A. Karoly, Application note an#409 determining the sequence of crossed lines by ft-ir-atr-microscopy. (accessed: 18.10.2017).

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