Process Of Creating Real-time Deep Fake Video Chatbot

Process Of Creating Real-time Deep Fake Video Chatbot
In the recent times, you will definitely have been hearing about the deepfake videos. Deepfakes are an advanced technology that comprises AI and deep learning that could exhibit either manipulated videos or images that seem real. They have obtained wide recognition and fame since they possess enjoyment features.
The effect of deepfake can be very mind-boggling and real enough, so viewers may have the challenging task of identifying reality from a deepfake.

Introduction to Deepfake

Deepfake is a kind of artificial media produced with the help of machine learning. It is applied to replace or even maliciously alter anyone’s likeness in pictures, videos, and voice recordings.

The development of the real-time deep fake interactive video chatbot implies deferred mastering of deep learning and AI technologies. Through the organized construction and application of the most up-to-date methods and tools, the developers can craft a realistic and fascinating chatbot experience fingerprinting boundaries of reality and virtual.
Responsible and ethical considerations are paramount in the development of deep fake technology, given its potential implications on privacy, misinformation, and social dynamics. Careful planning and execution are necessary to mitigate risks and ensure positive outcomes.
Deepfakes rely on advanced deep learning algorithms such as Generative Adversarial Networks (GANs) to generate highly realistic and convincing representations of individuals’ faces, voices, or behaviours.
While deepfakes offer potential applications in entertainment and advertising, they also pose significant ethical concerns due to their capacity for content manipulation.
Unlike traditional forms of fake media, deepfakes are notoriously challenging to detect, amplifying their potential for spreading misinformation and causing harm.
Introduction to Deepfake​

History and Development of Deepfake Technology

The development of this technology started from simple video modification techniques to today’s present AI-driven video methods.
In the beginning, video editing utility programs were employed to manipulate and rearrange images and footage that ultimately created the virtual world. Nevertheless, the label “deepfake” reached its global recognition thanks to one Reddit user, known as “Deepfakes” in 2017, because he uploaded more realistic movies on his account. This is the first-ever pervasive usage of the Deep Learning technology during which these videos were fictitiously distorted, and hence everyone was attuned to this newly-developed technology.
As AI technology progressed, the development of deepfake technology witnessed significant milestones, especially with the adoption of Generative Adversarial Networks (GANs). Ian Goodfellow and his colleagues introduced GANs in 2014, which constitute a breakthrough in the area, enabling for more sophisticated and believable deepfakes.
Deepfake technology nowadays is based on more intricate neural networks, which need a tonne of data and processing power to produce their incredibly lifelike fake material. To control the effects of deepfakes on society, ongoing research on detection technologies and ethical frameworks is essential. This quick advancement presents both benefits and dangers.

How Does it Work?

The main concept behind the deepfake technology is the facial recognition. The people using Snapchat will be quite familiar with this concept and make use of the face swap of filter functions of the app. Deep fakes are similar to that but they are much more realistic than the former.
The fake videos are created by the use of a machine learning technique called ‘Generative Adversarial Network’ or GAN. A Generative Adversarial Network is capable of looking at thousands of pictures and then producing a new image that approximates those pictures without being exactly similar to any one of the photos.
GAN is a multipurpose technology that can be used to create new text from existing text or new audio from existing audio. Deep Fakes are produced by technology that maps faces based on “landmark” points. These are the contours of your jawline, your nostrils, and the corners of your lips and eyes.

How Does it Work?

The main concept behind the deepfake technology is the facial recognition. The people using Snapchat will be quite familiar with this concept and make use of the face swap of filter functions of the app. Deep fakes are similar to that but they are much more realistic than the former.
The fake videos are created by the use of a machine learning technique called ‘Generative Adversarial Network’ or GAN. A Generative Adversarial Network is capable of looking at thousands of pictures and then producing a new image that approximates those pictures without being exactly similar to any one of the photos.
GAN is a multipurpose technology that can be used to create new text from existing text or new audio from existing audio. Deep Fakes are produced by technology that maps faces based on “landmark” points. These are the contours of your jawline, your nostrils, and the corners of your lips and eyes.

Creation of Deepfakes

Deepfakes, once considered complex and resource-intensive, are now more accessible thanks to advancements in artificial intelligence. With the latest breakthroughs in neural networks, it’s now possible to generate convincing deepfakes using just a small source video of the person.

Let’s delve into the two key components of creating deepfakes: voice cloning and video lip-syncing.

Voice Cloning

Voice cloning involves replicating a person’s voice using deep learning techniques. One prominent framework for voice cloning is SV2TTS:
SV2TTS is a deep learning framework in three stages. In the first stage, one creates a digital representation of a voice from a few seconds of audio. In the second and third stages, this representation is used as a reference to generate speech given arbitrary text.
Here’s how the process works:

Speaker Encoder: The process begins with the Speaker Encoder receiving the audio of the target person, which is extracted from the source video. The Speaker Encoder then encodes the audio input and generates embeddings that capture the unique characteristics of the person’s voice. These embeddings are passed to the Synthesizer for further processing.

Synthesizer: The Synthesizer is trained on the target audio data along with pairs of text transcripts. It learns to synthesize speech by mapping the input text to corresponding spectrograms, which represent the acoustic features of the speech signal. Using the embeddings from the Speaker Encoder, the Synthesizer generates spectrograms that mimic the voice of the target person.

Neural Vocoder: The spectrograms generated by the Synthesizer are passed to the Neural Vocoder, which converts them into output waveforms. The vocoder reconstructs the speech signal from the spectrogram representations, producing high-quality audio that closely resembles the voice of the target person. The resulting audio waveform is then combined with the synthesized video to create a deepfake video with synchronized speech.

Neural Vocoder
There are also some famous alternatives for real-time voice cloning other than SV2TTS:
is a token-infilling neural codec language model, that achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on in-the-wild data including audiobooks, internet videos, and podcasts.
  • Voice Craft is the most popular Web-based alternative to Real-Time Voice Cloning.
  • Voice Craft is the most popular open-source & free alternative to Real-Time Voice Cloning.
VoiceCraft Features
  • Ad-Free: VoiceCraft doesn’t contain any form of external advertising.
  • VoiceCraft allows you to transform written text into spoken text.
It offers high-quality pre-made voices, a Voice Design feature that allows you to create unique voices, and two different types of voice cloning features: Instant Voice Cloning and Professional Voice Cloning.
ElevenLabs Features
  • Text-to-Speech ElevenLabs allows you to transform written text into spoken text.
  • Ad-Free ElevenLabs doesn’t contain any form of external advertising.
  • ElevenLabs supports dark mode for comfortable usage in low-light conditions.
  • Lightweight ElevenLabs consumes fewer device resources compared to similar models.
  • Text-to-Video Narration ElevenLabs uses AI models to narrate your text/script input, setting the language, tone, regional dialect, and emotion, among others.

Video Lip Syncing:

In addition to voice cloning, video lip-syncing plays a crucial role in creating realistic deepfake videos. Lip syncing involves synchronizing the movements of the lips in the generated video with the audio, ensuring that the speech appears natural and coherent. While lip-syncing techniques vary, deep learning models such as First Order Motion Model (FOMM) have shown promising results in achieving accurate lip movements synchronization.
By combining voice cloning with video lip-syncing techniques, creators can generate deepfake videos that not only feature the person’s voice but also accurately replicate their facial expressions and lip movements, resulting in highly realistic simulations. Wav2lip is a lip-syncing Generative Adversarial Network (GAN) that specializes in synchronizing lip movements with audio inputs.
Here’s how the process works:

Input Audio and Video: Wav2lip takes as input an audio sample and an equal-length video sample of a person talking. The audio sample contains the desired speech that the person should be lip-synced to.

Lip Syncing: Using the input audio and video, Wav2lip employs a GAN architecture to analyze and synchronize the lip movements of the person in the video with the provided audio. The model adjusts the lip movements frame by frame to match the timing and articulation of the speech in the audio input.

Output Video: After lip-syncing is performed, Wav2lip generates a synthetic video in which the person appears to be speaking the input audio instead of the original audio from the sample video. The resulting video retains the visual appearance and expressions of the person while accurately syncing the lip movements with the provided speech.

Output Video
There are also some famous alternatives for real-time voice cloning other than Wav2lip:

Here are the Step-by-Step Instructions to create a DeepFake video chatbot with sources from scratch:

Data Collection: You’ll need to gather high-quality images and videos of the person whose face you want to swap, as well as a target video or audio where you want to insert the fake face or voice. This process may involve cropping, aligning, and editing to ensure the best results. Additionally, you’ll need a large dataset of video and audio recordings of the person you want to mimic, including various facial expressions, speech patterns, and gestures. This dataset will be used to train the deep learning model to create a realistic and convincing deepfake.

Preprocessing: This involves cleaning and preparing the data to ensure that it is of high quality and consistent. The first part of preprocessing is extracting frames and faces from the source and destination videos. This can be done using tools like DFL, which provides executable batch files for this purpose. These tools allow for options to be set and the process to run to completion, ensuring that the extracted frames and faces are ready for further processing.

The quality of the data is crucial for the success of the deepfake model. This means sifting through thousands of images to ensure they are of high quality and that the model has detected the faces properly. Sorting by blurriness, face similarity, histogram, and other factors can help group bad frames and miscaptured faces, making the process more efficient. At the end of the preprocessing step, you’ll have thousands of sliced-up frames and normalized faces, which are ready to be used for training the deep learning model.

Model Selection: Choose a deep learning model for generating deep fake videos. Popular choices include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), or more advanced models like StyleGAN.

Below are some representations of their work:
Training of Autoencoders Create Deepfake

a) Training of Autoencoders Create Deepfake

Deepfake Creation using Autoencoder
(b)Deepfake Creation using Autoencoder
Training of GAN to Create Deepfake

(c)Training of GAN to Create Deepfake

Training: Train the selected model using the pre-processed data. This step can be computationally intensive and may require powerful hardware like GPUs.

Integration with Chatbot: Once you have a trained model, integrate it with a chatbot framework like Dialog Flow, Rasa, or Microsoft Bot Framework. This will allow the chatbot to interact with users and generate responses.

Real-time Processing: To achieve real-time deep fake video generation, you’ll need to optimize the model for speed. This might involve using techniques like model pruning, quantization, or running the model on specialized hardware. Pruning involves selectively deleting weights to reduce the size of a learning network. For example, neuron pruning removes entire neurons from a DNN, which is useful when neurons are consistently inactive or contribute minimally to the model’s output.

Pruning and quantization can reduce the inference time and memory footprint of a network, making it easier to deploy. However, the pruning process can cause the prediction accuracy to decrease. To improve prediction accuracy, the network can be retrained using a custom training loop.
Real-time Processing

Deployment: Deploy the chatbot and deep fake video generation model to a server or cloud platform like AWS, Azure, or GCP. Ensure that the system can handle the computational requirements of real-time video processing.

Monitoring and Maintenance: Regularly monitor the chatbot for performance and accuracy. Update the deep fake model as needed to improve the quality of generated videos.

Ethical Considerations:

When creating a real-time deepfake video chatbot, it’s essential to consider the ethical implications of the technology. Here are some key ethical considerations to keep in mind:
  • Encryption at rest: Some AWS products like Amazon S3, EBS, and RDS can be used properly in order to have built-in options for data encryption which will ensure data is protected at rest. Customers will be able to perform a full range of encryption key management i.e. creating them, controlling them, and searching for them thanks to the AWS KMS.
  • Encryption in Transit: Apply the strict certification rules and algorithmic processes like SSL/TLS to data in transit. For instance, HTTPS encryption protects sensitive information that is passively/actively communicated to and from Amazon S3.

Regular Updates and Patching

It is essential for security to update and patch software and AWS resources on a regular basis:
  • Privacy: Respect the privacy of individuals whose likeness is used in deepfake videos. Ensure that you have the necessary permissions and rights to use their images or videos.
  • Misuse: Be aware of the potential for misuse of deepfake technology. Take steps to prevent the creation of deepfake videos that could be used for malicious purposes, such as spreading misinformation or manipulating individuals.
  • Transparency: Be transparent about the use of deepfake technology in your chatbot. Users should be informed if they are interacting with a bot that uses deepfake videos.
  • Accuracy: Ensure that the deepfake videos generated by your chatbot are accurate and do not misrepresent individuals or events.
  • Security: Implement security measures to prevent unauthorized access to the deepfake video creation process and to protect the integrity of the videos.
  • Legal Compliance: Ensure that your use of deepfake technology complies with relevant laws and regulations, including those related to privacy, data protection, and intellectual property rights.

Impact of Deepfakes:

The impact of deepfakes is multifaceted, with both positive and negative implications depending on their use. While the potential for misuse is concerning, there are also beneficial applications of deepfake technology that deserve recognition.

Pros of Deepfakes:

Artistic Expression: Deepfakes can serve as a medium for artistic expression, enabling the creation of unique and engaging content. For example, historical figures or iconic paintings can be brought to life through synthetic videos, offering a new perspective on art and history.

Training and Education: Deepfake technology has practical applications in training and education. Organizations like Synthesia utilize AI avatars in training videos, providing an alternative to traditional video shoots, especially beneficial during periods of lockdowns and health concerns.

Personalization: Deepfakes offer opportunities for personalization, allowing individuals to create virtual avatars for various purposes. From trying on clothes or hairstyles to enhancing privacy and identity protection, deepfake technology enables personalized experiences in diverse fields.

Cons of Deepfakes:

Spread of Misinformation: One of the most significant concerns surrounding deepfakes is their potential to spread misinformation. Morphed videos of celebrities or public figures can be used to propagate fake news, leading to confusion and mistrust among the public.

Manipulation on social media: Deepfakes can be exploited for malicious purposes on social media platforms. Misinformation campaigns fuelled by morphed videos can manipulate public opinion and have far-reaching consequences, undermining the integrity of democratic processes and societal trust.

In conclusion, while deepfake technology presents exciting possibilities for creativity and innovation, it also raises important ethical and societal challenges. It is essential to approach its development and use with caution, considering its potential impacts on individuals, communities, and the broader society. By promoting responsible practices and awareness, we can harness the benefits of deepfakes while mitigating their risks.


Deepfake video chatbots have the potential to revolutionize various sectors, including IT, healthcare, and beyond, by offering innovative solutions and enhancing user experiences in diverse ways. Here’s how deep fake video chatbots can be beneficial in different sectors:

IT Sector:

Virtual IT Support Agents: Deepfake video chatbots can serve as virtual IT support agents, providing real-time assistance and troubleshooting to users. These chatbots can simulate interactions with human agents, offering personalized guidance and solutions for IT-related issues.
Training and Education: In the IT sector, deepfake video chatbots can be used for training purposes, providing interactive tutorials, demonstrations, and simulations. IT professionals can learn new technologies and processes in a dynamic and engaging manner through simulated interactions with virtual trainers.
Product Demonstrations: Companies in the IT industry can leverage deepfake video chatbots to demonstrate their products and services in a more interactive and immersive way. These chatbots can showcase product features, functionalities, and use cases through simulated conversations and demonstrations.

Healthcare Sector:

Virtual Health Assistants: Deepfake video chatbots can serve as virtual health assistants, providing patients with personalized health advice, medication reminders, and symptom tracking. These chatbots can simulate conversations with healthcare professionals, offering support and guidance to patients remotely.

Medical Training and Education: Healthcare professionals can benefit from deepfake video chatbots for medical training and education. These chatbots can simulate patient interactions, medical consultations, and diagnostic scenarios, allowing healthcare students to practice and improve their skills in a simulated environment.

Telemedicine: Deepfake video chatbots can enhance telemedicine services by providing patients with virtual consultations and remote healthcare support. These chatbots can assist healthcare providers in conducting virtual appointments, gathering patient information, and delivering medical advice in real time.

Other Sectors:

Customer Service: Deepfake video chatbots can be deployed in various industries for customer service and support. Companies can use these chatbots to interact with customers, answer queries, and help across different channels, enhancing the overall customer experience.

Marketing and Advertising: Deepfake video chatbots can be utilized in marketing and advertising campaigns to engage with audiences in a more personalized and interactive manner. These chatbots can deliver targeted messages, promotions, and product recommendations through simulated conversations and interactions.

Entertainment and Media: Deepfake video chatbots can offer unique entertainment experiences, such as virtual celebrities, interactive storytelling, and immersive gaming. Users can engage with virtual characters and personas, creating personalized experiences and entertainment content.


In conclusion, deepfake technology may also contribute to the functioning of the creative and innovation industries. There is artistic expression, training, personalization, and more, too, and this evokes the question as to whether it serves for disinformation and manipulation distribution. Properly made and applied, such technologies, while ensuring the citizens’ safety and protecting the stability of our world, can bring new persistent benefits.
The service of deepfake video chatbots is an untold story. For instance, it can be used in the fields of IT, health & medicine, customer service, and entertainment, and for many more purposes.
Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

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