Abstract

This proposal presents a final year project in computer science focusing on the development of a deep fake detection system. With the rapid advancement of deep learning techniques, the creation and dissemination of manipulated media, known as deep fakes, pose significant threats to various societal domains, including misinformation, privacy infringement, and cybersecurity. This project aims to address these challenges by leveraging state-of-the-art machine learning algorithms and computer vision techniques to detect and mitigate the spread of deep fakes. Through the integration of robust detection mechanisms and real-time monitoring capabilities, the proposed system aims to empower users and organizations to identify and combat the proliferation of manipulated media content effectively.

Introduction

Deep fake technology has emerged as a formidable tool for generating hyper-realistic synthetic media, including images, videos, and audio recordings, often indistinguishable from authentic content. The widespread availability of deep fake generation tools and the ease of dissemination through online platforms have raised concerns regarding the potential misuse of this technology for malicious purposes, such as spreading misinformation, manipulating public opinion, and impersonating individuals. In response to these threats, there is a pressing need for effective deep fake detection systems capable of identifying and mitigating the harmful effects of manipulated media.

Problem

The proliferation of deep fake content presents multifaceted challenges across various domains, including journalism, entertainment, politics, and cybersecurity. Traditional methods of media authentication and verification are often inadequate in detecting deep fakes due to their sophisticated and realistic nature. As a result, individuals and organizations are vulnerable to the dissemination of false or misleading information, leading to potential reputational damage, privacy violations, and social unrest. The lack of robust and scalable deep fake detection solutions exacerbates these challenges, necessitating innovative approaches to address the evolving threat landscape.

Aim

The primary aim of this project is to develop a comprehensive deep fake detection system capable of accurately identifying manipulated media content across different modalities, including images and videos. By leveraging advanced machine learning algorithms and computer vision techniques, the system seeks to analyze subtle artifacts and inconsistencies inherent in deep fake creations, enabling reliable detection and classification of synthetic media. Furthermore, the project aims to provide users with intuitive tools and interfaces for interactively exploring and verifying media authenticity, thereby empowering individuals and organizations to make informed decisions in an increasingly digital and interconnected world.

Objectives

1. To conduct a thorough review of existing literature and state-of-the-art techniques in deep fake generation and detection.

2. To collect and curate diverse datasets of authentic and manipulated media content for training and evaluation purposes.

3. To explore and implement advanced machine learning models, such as deep neural networks, for deep fake detection and classification.

4. To design and develop a scalable and efficient deep fake detection system capable of processing multimedia inputs in real-time.

5. To evaluate the performance and robustness of the system through extensive testing with benchmark datasets and real-world scenarios.

6. To integrate the deep fake detection system with user-friendly interfaces and feedback mechanisms for usability and effectiveness assessment.

7. To disseminate the findings and insights through research publications, workshops, and collaborations with industry partners and stakeholders.

Research

The proposed research will leverage cutting-edge techniques from machine learning, computer vision, and multimedia forensics to address the challenges of deep fake detection. The initial phase will involve data collection and preprocessing, including the acquisition of authentic and manipulated media samples from various sources. Subsequently, deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), will be explored and adapted to effectively distinguish between real and synthetic media content. The development of the detection system will entail the integration of feature extraction, classification, and anomaly detection techniques to identify subtle cues indicative of deep fake manipulation. Rigorous evaluation and benchmarking will be conducted using established metrics and protocols, including detection accuracy, false positive rate, and computational efficiency. Moreover, the research will encompass ethical considerations, such as privacy preservation and bias mitigation, to ensure the responsible deployment and use of deep fake detection technologies in real-world settings.