Creating My AI Clone: A Revolutionary Social Engineering Experiment

Table of Contents

1. Introduction
2. Background
3. Using AI to Clone Myself in Digital Chat
   • How it Started
   • The Science of Behavioral Analytics
   • Protecting Your Identity
4. The Inspiration
   • Identity Fraud Prevention
   • Replicating an Identity
   • Black Mirror Episode: "Be Right Back"
5. Creating the Chatbot
   • Categorizing the Conversations
   • Using Rasa for Chit Chat and Historical Data
   • Using ChatterBot for Topic-Based Conversations
   • Open Domain Chatbots: Parlia and DialoGPT
6. Voice Cloning
   • TTS Models: Tacotron and DeepSpeech
   • Using Google Text-to-Speech Service
   • Training with CycleGAN Model
7. Facial Expression and Lip Syncing
   • Using LeapGAN Model
   • Creating Real-time Cloned Video
8. Bringing it all Together
   • Architecture Overview
   • Stitching Voice and Video Components
   • Real-time Demo with LSu
   • Future Improvements and Roadmap
9. Conclusion
10. References

Introduction

In this article, I will discuss how I use AI to clone myself in digital chat. I will provide a brief background about myself and my experience in the industry. Then, I will explain the concept of behavioral analytics and how it can be used to protect your identity. Next, I will discuss the inspiration behind this project, including the idea of replicating an identity and the influence of the TV Show Black Mirror.

Background

As the CTO and co-founder of Neoid, I have extensive experience in the fields of cybersecurity and machine learning. I have worked in various domains such as finance, insurance, gaming, and eCommerce. Throughout my career, I have been a frequent speaker at conferences and have been involved in community activities to learn from and give back to the community.

Using AI to Clone Myself in Digital Chat

How it Started

The idea of using AI to clone myself in digital chat came to me while working on behavioral analysis in New York. We were using behavioral analysis to protect identities and prevent identity fraud. This got me thinking about the possibility of using AI to replicate an identity and Create a digital clone.

The Science of Behavioral Analytics

Behavioral analytics involves profiling a person based on their behavior and habits. This includes analyzing how they Type, hold their phone, and even how they walk. By capturing these unique parameters, we can create a behavioral profile to protect identity and identify impersonators.

Protecting Your Identity

The primary objective of this project is to use AI to identify and prevent fraud and impersonation. By analyzing past behavior and creating a behavioral persona, we can detect if someone is trying to impersonate a person based on their behavior.

The Inspiration

The inspiration for this project came from a Black Mirror episode called "Be Right Back." The episode tells the story of a woman named Martha who uses a futuristic company's services to bring back her deceased boyfriend through digital chat interfaces, phone calls, and eventually a 3D model. This episode sparked my interest in exploring the possibility of creating a similar Avatar or digital clone.

Identity Fraud Prevention

While working on behavioral analysis to prevent identity fraud, the idea of cloning an identity became a natural extension. By using AI and behavioral analytics, we can not only prevent impersonation but also replicate an identity in digital chat.

Replicating an Identity

The concept of replicating an identity using AI was intriguing to me. I wanted to explore if it was possible to create a digital clone with whom one could have a conversation in text, voice, and even video.

Creating the Chatbot

To create the chatbot, I categorized the conversations into different types: chit chat, historical data, topical discussions, and free flow discussions. For chit chat and historical data, I used the Rasa framework. For topical discussions, I used ChatterBot. When it came to free flow discussions, I experimented with two models: Parlia and DialoGPT. Each model had its strengths and weaknesses in generating human-like responses.

Categorizing the Conversations

To better organize and generate responses, I categorized the conversations into four categories: chit chat, historical data, topical factual data, and free flow philosophical discussions. This allowed me to train models specific to each category and generate more accurate responses.

Using Rasa for Chit Chat and Historical Data

For chit chat and historical data conversations, I used the Rasa framework. Rasa allowed me to train models based on past conversations and generate responses based on the Context of the conversation.

Using ChatterBot for Topic-based Conversations

For topical factual data conversations, I used ChatterBot. ChatterBot allowed me to create question-answer pairs based on different topics. This gave the chatbot the ability to provide factual information and engage in topic-specific discussions.

Open Domain Chatbots: Parlia and DialoGPT

For free flow philosophical discussions, I explored two models: Parlia and DialoGPT. These models allowed for more open-ended conversations and generated responses based on broader contexts. However, more work is needed to improve the performance and coherence of these models.

Voice Cloning

Voice cloning was an essential aspect of creating a realistic chatbot experience. I experimented with different models such as Tacotron, DeepSpeech, and Google Text-to-Speech. However, the best results were achieved with a training using the CycleGAN model.

TTS Models: Tacotron and DeepSpeech

Tacotron and DeepSpeech are popular models used in text-to-speech (TTS) applications. These models require a significant amount of data and several hours of training to achieve realistic voice cloning.

Using Google Text-to-Speech Service

To overcome the limitations of the previous models, I utilized the Google Text-to-Speech service. This service allowed me to generate a voice that closely resembled mine and was more convincing in the chatbot conversations.

Training with CycleGAN Model

To improve the voice cloning process, I used the CycleGAN model. This model is capable of transferring voice properties and creating a clone voice with less training data. The training is still ongoing, and improvements are expected in the future.

Facial Expression and Lip Syncing

To further enhance the chatbot experience, I worked on replicating facial expressions and lip syncing. I experimented with the LeapGAN model, which showed promising results in generating realistic facial movements.

Using LeapGAN Model

The LeapGAN model uses audio input to generate realistic facial movements and expressions. By training the model on a large dataset, it can accurately mimic facial expressions based on the audio input.

Creating Real-time Cloned Video

By combining the voice and facial expression models, I was able to create near-real-time cloned videos. These videos synchronized the speech generated by the voice model with realistic facial expressions, creating a more immersive chatbot experience.

Bringing it all Together

To bring all the components together, I created an architecture that incorporated the chat engine, voice engine, and face engine. The chat engine generated responses based on user input, the voice engine converted the text to speech, and the face engine synchronized the speech with facial expressions.

Architecture Overview

The architecture consisted of an API gateway, which received user input from messaging platforms like WhatsApp and Telegram. The responses were generated by the chat engine, which used Rasa for chit chat and historical data, ChatterBot for topic-based discussions, and open-domain chatbots for philosophical discussions. The voice engine utilized TTS models and the face engine utilized the LeapGAN model.

Stitching Voice and Video Components

To create a seamless chatbot experience, I used a video streamer called CamTwist. This allowed me to switch between silent and talking videos based on the user's input. The silent video displayed when the chatbot was listening, while the talking video displayed when the chatbot was speaking.

Real-time Demo with LSu

To demonstrate the capabilities of the chatbot, I conducted a real-time demo with LSu, a co-founder of Neoid. The chatbot engaged in a conversation, displaying chit chat, historical data, and free flow philosophical discussions. The voice and facial expression models were also utilized to create a more lifelike experience.

Future Improvements and Roadmap

Although the chatbot prototype shows promising results, there are still areas for improvement. The facial expressions and lip syncing can be further enhanced to create a more realistic experience. Additionally, bypassing biometrics and incorporating other emotions are potential areas for future development.

Conclusion

Using AI to clone oneself in digital chat opens up a world of possibilities for personalization and engagement. By combining behavioral analytics, chatbot technology, voice cloning, and facial expression synthesis, we can create a more immersive and lifelike chatbot experience. While there is still work to be done to improve the technology, the results so far are promising. This technology has the potential to revolutionize digital interactions and open up new avenues for expression and connection.

References

• [Reference 1]
• [Reference 2]
• [Reference 3]
• [Reference 4]
• [Reference 5]

Please note that the references Mentioned above are placeholders and need to be replaced with actual references.

Note: The headings and subheadings in the generated table of Contents may need to be adjusted for formatting and accuracy.