Multimedia Security

This FFT-75 dataset contains randomly sampled, potentially overlapping file fragments from 75 popular file types (see details below). It is the most diverse and balanced dataset available to the best of our knowledge. The dataset is labeled with class IDs and is ready for training supervised machine learning models. We distinguish 6 different scenarios with different granularity and provide variants with 512 and 4096-byte blocks. In each case, we sampled a balanced dataset and split the data as follows: 80% for training, 10% for testing and 10% for validation.

  • Security
  • Last Updated On: 
    Wed, 08/07/2019 - 16:56

     In this research, we propose an intelligent watermarking technique called ANiTH which conceals an invisible watermark through a digital text such that the hidden watermark can be extracted whenever there is an inquiry about the accuracy and reliability of digital texts shared on social media.

  • Reliability
  • Last Updated On: 
    Fri, 02/08/2019 - 00:19
    Citation Author(s): 
    Milad Taleby Ahvanooey, and Qianmu Li