Digital Watermarking for Protecting Audio Classification Datasets

Project Code :TMMASP06

Objective

In this study, we investigate the possibility of protecting audio classification datasets used in deep learning by embedding a pattern in the magnitude of the time-frequency representation of a subset of the dataset

Abstract

In this study, we investigate the possibility of protecting audio classification datasets used in deep learning by embedding a pattern in the magnitude of the time-frequency representation of a subset of the dataset. Previous studies on audio watermarking technologies require the actual sound of the watermarked audio to extract the information embedded in it.

 In our study, we propose an audio watermarking framework aimed to identify whether a deep learning based audio classification model is trained with the watermarked audio classification dataset or not by using only the classification results. The experimental results show that our proposed method can identify the usage of an audio classification dataset while having minimal effect on the overall classification performance.

 

Keywords: Deep Learning, Audio Watermark, Audio Classification, Dataset Protection, Time-Frequency Representation.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software & Hardware Requirements:

Software:

MATLAB R2018a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    • Morphological processing
    • Segmentation etc.,
  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will able to know, what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:
    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network
  • How to protect and classify the audio using AI
  • How to extend our work to another real time applications
  • Project development Skills:
    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

Demo Video

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