ZKHack

Problem: One of the major challenges in hackathons is the issue of participants copying and submitting existing projects that have already been presented in previous hackathons. This practice not only undermines the purpose of hackathons, which is to foster creativity and innovation, but also creates an unfair advantage for those who resort to plagiarism. Detecting such cases manually can be time-consuming and prone to human error, making it necessary to find a more efficient and reliable solution.

Solution: To address this problem, our project proposes an AI-based solution that utilizes advanced techniques to identify and flag potentially plagiarized projects. We employ natural language processing (NLP) algorithms to convert project descriptions and other relevant data into textual representations enriched with keywords that capture the unique nature of the project idea. This allows for efficient comparison and analysis.

When a new project is submitted, it undergoes a comprehensive cross-check with all existing ideas stored in the database. Utilizing a combination of machine learning and zero-knowledge (ZK) concepts, an AI model is employed to determine similarities between the submitted project and the existing ones. The ZK approach ensures that sensitive project information remains encrypted and protected during the comparison process.

Projects that exhibit a similarity score of 70% or higher are automatically flagged and moved to a designated voting gallery. In this gallery, all participants can engage in a decentralized autonomous organization (DAO) proposal format, where they have the opportunity to vote on whether they believe the project is plagiarized and deserves to be disqualified. This democratic approach empowers the hackathon community to collectively make informed decisions about project authenticity.

To ensure fairness and accuracy, the voting period lasts for a predefined time period, typically three days. At the end of the voting period, if the number of downvotes surpasses the number of upvotes, the project is deemed plagiarized and disqualified from the hackathon. This process prevents any potential biases and provides a transparent and community-driven resolution.

By implementing this comprehensive AI-driven system, we aim to uphold the integrity of hackathons, promote originality and creativity, and create a fair playing field for all participants. It not only streamlines the detection of plagiarized projects but also fosters a sense of collaboration and trust within the hackathon community.

Our plagiarism detection system for hackathons involves a combination of technologies and processes to ensure accuracy and efficiency. Here is a detailed analysis of how it is made and the technologies used:

1. Data Collection: We gather project descriptions, relevant data, and other textual information from previous hackathons to build a comprehensive database of existing ideas. This data serves as a reference for comparison.

2. Natural Language Processing (NLP): We employ NLP techniques to preprocess and transform the textual data into a format suitable for analysis. This involves tasks such as tokenization, stemming, and removing stop words. NLP libraries such as NLTK (Natural Language Toolkit) or spaCy can be utilized for this purpose.

3. Keyword Extraction: To capture the unique nature of project ideas, we employ keyword extraction algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency) or TextRank. These algorithms identify the most significant keywords that represent the essence of each project description.

4. Project Representation: The extracted keywords are combined with other relevant project data to form a concise textual representation of each project. This representation serves as the basis for project comparison and similarity measurement.

5. Machine Learning Model: We train a machine learning model, such as a text similarity model or a Siamese neural network, using a labeled dataset of known plagiarized and original projects. This model learns to identify similarities between projects based on their textual representations. Popular frameworks like TensorFlow or PyTorch can be utilized for model development and training.

6. Zero-Knowledge (ZK) Encryption: To protect the sensitive project information during comparison, we employ ZK encryption techniques. ZK protocols allow us to compare encrypted data without revealing the actual content, ensuring privacy and security.

7. Similarity Calculation: The machine learning model computes the similarity score between a newly submitted project and the existing ones in the database. This score indicates the degree of similarity between the projects and helps identify potential cases of plagiarism.

8. Flagging and Voting Gallery: Projects with a similarity score equal to or above a predetermined threshold, such as 70%, are flagged as potential plagiarized projects. These flagged projects are then moved to a dedicated voting gallery for further evaluation.

9. Decentralized Voting: In the voting gallery, participants engage in a decentralized voting process using a DAO proposal format. This allows participants to review the flagged projects and vote on whether they believe a project is plagiarized or not. This approach ensures fairness and transparency in decision-making.

10. Voting Period and Disqualification: A predefined voting period, typically three days, is provided for participants to cast their votes. At the end of the voting period, the project's fate is determined based on the vote count. If the number of downvotes exceeds the number of upvotes, the project is disqualified from the hackathon.

The system utilizes a combination of technologies including NLP, machine learning, ZK encryption, and decentralized voting to create a robust and efficient solution for plagiarism detection in hackathons. By leveraging these technologies, we can foster an environment of fairness, originality, and trust within the hackathon community, ensuring the integrity of the competition.