Decentralized Deepfake Detection Blockchain Network using Dynamic Algorithm management

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Authors: Dipankar Sarkar

Published: 2023-11-30 13:27:00+00:00

AI Summary

This paper proposes a decentralized blockchain-based system for deepfake detection. The system integrates deep learning algorithms with smart contracts for dynamic algorithm management and token-based incentives, creating a trustless environment for verifying digital media authenticity.

Abstract

Deepfake technology is a major threat to the integrity of digital media. This paper presents a comprehensive framework for a blockchain-based decentralized system designed to tackle the escalating challenge of digital content integrity. The proposed system integrates advanced deep learning algorithms with the immutable and transparent nature of blockchain technology to create a trustless environment where authenticity can be verified without relying on a single centralized authority. Furthermore, the system utilizes smart contracts for dynamic algorithm management and token-based incentives further enhances the system's effectiveness and adaptability. The decentralized architecture of the system democratizes the process of verifying digital content and introduces a novel approach to combat deepfakes. The collaborative and adjustable nature of this system sets a new benchmark for digital media integrity, offering a more robust digital media environment.

Key findings

The paper presents a framework; no experimental results are included. The proposed system offers a decentralized, adaptable, and incentivized approach to deepfake detection. However, limitations such as scalability and real-time processing are acknowledged.

Approach

The system uses a decentralized blockchain architecture to record deepfake detection results. Smart contracts manage algorithm selection and incentivize contributions. Deep learning models (CNNs, GANs, RNNs) are used for audio and video analysis, with results reported to the blockchain.

Datasets

UNKNOWN

Model(s)

Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and their variants.

Author countries

UNKNOWN

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