saifuljnu / a-blockchain-based-digital-classified-forensic-image-preservation-framework

Research work

A-Blockchain-Based-Digital-Classified-Forensic-Image-Preservation-Framework

Research work

How does the system work? (in brief)

After the police investigation for the digital evidence, the authorized investigator can able provide the sensitive forensic image as input to the system then the system will classify the evidence according to the pre-trained deep learning model.

After that, the image will be stored in the IPFS storage with the important details such as case number, insertion time, case details, etc. This is how we will obtain the documentation and classification features of Digital forensics.

Now time to showcase the file to the judges to do so, we need to go through the access control process, there will be N number of authorized people to present the evidence from the system But for the confidential issue they need to participate in the voting system, After successfully submitting the vote the winner will be the presenter.

To classify the evidence

At first install the flask & Keras model then run the following command

python app.py

Now you will find a url in the command promt . Then just go through the Image Classification UI. To classify the image you need to insert image as a input.

To secure the classified evidence

Now for securing the classified image through BlockChain we need to follow the rules and run the command . Rules: ***We need to have an account in metamask wallet with Gurli testnet. After that

npm install
npm start

Now you will have a url in the command promt . Then just go through the Decentralised Storage for Digital Forencics Evidence. Then the system admin can able to set the documentation of the evidence (This evdence will be stored in IPFS)

---

For access control

In this fragment of this project we are ensuring the access control through the voting system. now to run this fragment you need to write following command

    npm install
    npm run dev
    

here is the same analogy for url and UI

---