#Post Covid Hack 2020


DeDonate will help people to donate to a person directly thus removing the frauds and faulty corrupted middleman. It helps charitable org to raise funds for a social cause with help of RSK blockchain.

  • DeDonate has a fraud request detection using AI/Ml
  • AI/ML is used to verify the identity of the requester for donation to prevent fake donations and maintain authenticity in the system. We have used image processing to implement this verification wherein we ask the user to upload an image of his/her id card and at the same time of requesting we take a fresh click of his facial image through webcam.
  • we then compare the uploaded id card's facial image with the fresh clicked one and if they both differ, we identify the requester as fake/nonauthentic.

  • DeDonate has two modes of donation peer-to-peer and charity raise fund.

  • peer-to-peer:- Donations between the users in RBTC on RSK blockchain.

    1. A hardworking person/student will request for his/her essential, (for example a student who is unable to buy his/her desired books, but he/she is a brilliant student)
    1. Our Dapp will store it in RSK blockchain, later will display the request on the frontend once the user(requester) passes the fraud validation test.
    1. Users(donators) from our platform can pay for the required essential request if he/she thinks it's valid ( anonymously pay for the request on blockchain from his address to the requester's address)

  • charity-request:- An organization can raise funds in RBTC for some social cause.

    1. Charitable organization requests for fundraising.
    1. The organization sets an amount to be raised.
    1. According to the raise amount number of DDN tokens are minted and added to the total supply.
    1. The users donate to the social cause of a charity, according to their amount of donation, DDN tokens are distributed to users as a reward.

Challenges we faced

  • We wanted to keep the system as much as decentralized as possible so we made most of the process automated regarding the validation etc.
  • Combining and integrating the AI/ML python-based code with solidity.
  • Validation in Ml was causing trouble with their desired output types and content type.
  • The conversion values using web3 values in rsk network were a bit different, so we moved to rsk3.

What we learned

  • How does rsk node works.
  • Combining solidity and ai/ml.
  • Generation of fungible tokens.
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