Combining Genetic Algorithms and Neural Networks for File Forgery Detection

  • Konstantinos ΚarampidisEmail author
  • Ioannis Deligiannis
  • Giorgos Papadourakis
Part of the Intelligent Systems Reference Library book series (ISRL, volume 149)


Today’s electronic devices are so ubiquitous that the collection and use of digital evidence has become a standard part of many criminal and civil investigations. The uncovering and examination of those shreds of evidence is a relatively new and important process to provide crucial information in a court of law. Suspects routinely have their laptops and cell phones examined for corroborating evidence. However, digital forensic investigators are facing several challenges such as file obfuscation, encryption, alteration and a massive amount of evidence. These challenges often lead to incomplete analysis and inadequate conclusions. Consequently, a digital forensic examiner uses specialized forensic software to accurately identify the file types to determine which of them may contain potential evidence.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Konstantinos Κarampidis
    • 1
    Email author
  • Ioannis Deligiannis
    • 2
  • Giorgos Papadourakis
    • 3
  1. 1.Department of Information & Communication Systems EngineeringUniversity of the AegeanKarlovasi, SamosGreece
  2. 2.Department of Cultural Heritage Management and New TechnologiesUniversity of PatrasAgrinioGreece
  3. 3.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklion, CreteGreece

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