Detecting Tampered Regions in JPEG Images via CNN

Project Code :TMMAAI51


Digital pictures are used as evidence in criminal investigations. Therefore, it is essential to check whether they have been tampered with or not. In this study, we propose a method for detecting the tampered region in a JPEG image by using a convolutional neural network (CNN).


In this work, we will detect the tampered regions in JPEG images using deep learning techniques (U Net architecture). Often, digital pictures are used as evidence in criminal investigations. Therefore, it is essential to check whether they have been tampered with or not. DCT coefficients play an important role in the detection of Tampered regions of images and these DCT coefficients are input to the CNN. 

Convolutional neural network (U Net architecture) has been successfully used to achieve good performance in detecting tampered regions of images. U-net is a convolutional network architecture for fast and precise segmentation of images mainly. Experiments will demonstrate that our model will provide the best detection performance compared to the state-of-the-art methods.

Keywords: Tampered Image, JPEG, Convolutional Neural Networks

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

Block Diagram


Software & Hardware Requirements:

Software: Matlab 2018a or above


Operating Systems:

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


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


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 Math Works products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • 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 be 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 detect the regions 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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