What is a Deepfake?

April 30, 2024

The elusive definition of a deepfake

The Elusive Definition of the Deepfake:

In the rapidly evolving landscape of artificial intelligence (AI) and synthetic media, the term "Deepfakes" has become increasingly prevalent. However, despite its widespread use, the definition of a Deepfake remains elusive and often inconsistent across various sources. In this blog post, we'll delve into the history of Deepfakes, examine the available definitions, and propose a comprehensive framework that captures the essence of this complex phenomenon.

The Origins of Deepfakes 

The term "Deepfakes" can be traced back to the end of 2017 when a Reddit user named "Deepfakes" began sharing manipulated videos on the platform. This user, along with others in the subreddit r/Deepfakes, created and shared videos that involved swapping celebrities' faces onto the bodies of actors in pornographic content. The subreddit's name, "Deepfakes," eventually became the term used to describe this type of AI-generated or manipulated media.

While the early days of Deepfakes primarily focused on face swapping, it quickly became apparent that the technology could be extended to create convincing fake audio using AI models. This realization opened up a new frontier in the world of synthetic media, with the potential for both positive applications and malicious misuse.

The Inconsistency of Deepfake Definitions 

As the concept of Deepfakes gained traction, various sources attempted to define the term. However, these definitions often lacked consistency and logical coherence. For instance, Wikipedia defines Deepfakes as "synthetic media in which a person in an existing image or video is replaced with someone else's likeness." While this definition captures the essence of face swapping, it fails to encompass the broader scope of AI-generated media, such as synthetic audio or entirely fabricated video content.

Similarly, Dictionary.com defines Deepfakes as "a video digitally altered to appear authentic, often in order to misrepresent the subject's speech or actions." This definition emphasizes the potential for misrepresentation but neglects to mention the role of AI in the creation process.

The lack of a clear and comprehensive definition of Deepfakes poses significant challenges in terms of public understanding, legal frameworks, and the development of countermeasures. It is crucial that we establish a more robust and inclusive definition to address these issues effectively.

The Importance of Distinguishing the Deepfake

 One of the key reasons why a clear definition of Deepfakes is so crucial is that it helps distinguish this technology from other forms of media manipulation and AI-generated content. In just the last few decades, various techniques have been used to alter or fabricate media, such as photoshopping images or cleverly editing videos. However, Deepfakes represent a fundamentally different approach, leveraging the power of AI to create highly realistic and convincing synthetic media.

It is essential to note that not all AI-generated content falls under the category of Deepfakes. For instance, language models like ChatGPT, which generate human-like text based on patterns learned from vast datasets, are not considered Deepfakes. While these models are capable of producing convincing and coherent text, they do not inherently involve the manipulation or generation of visual or auditory media in a way that may deceive or harm individuals.

Similarly, generative text models, such as GPT-3 or GPT-4, which can create original written content based on prompts or patterns, do not fit the definition of Deepfakes. These models are designed to assist with tasks like content creation, translation, or answering questions, rather than manipulating existing media or generating deceptive content.

By establishing a definition that emphasizes the role of AI in the creation process and distinguishes Deepfakes from other forms of AI-generated content, we can better understand the unique challenges posed by Deepfakes and develop targeted strategies to address them. This distinction is essential for policymakers, researchers, and the general public to grasp the full scope of the issue and respond accordingly.

Towards a Comprehensive Definition 

Drawing from the historical context and the limitations of existing definitions, we propose the following definition:

Deepfakes are images, audio, or videos that have been generated or manipulated by artificial intelligence (AI) in a way that may harm or mislead individuals.

This definition encompasses several key aspects:

  1. Scope: By including images, audio, and videos, the definition covers the full range of synthetic media that can be created using AI.

  2. Creation process: The definition explicitly mentions the role of AI in generating or manipulating the content, distinguishing Deepfakes from traditional media manipulation techniques.

  3. Potential for harm: By highlighting the potential for harm or deception, the definition acknowledges the ethical concerns surrounding Deepfakes and the need for responsible development and deployment of the technology.

Adopting this comprehensive definition can help foster a shared understanding of Deepfakes among researchers, policymakers, and the general public. It provides a foundation for developing effective countermeasures, establishing legal frameworks, and promoting responsible use of AI-generated media.


The definition of Deepfakes has been a subject of confusion and inconsistency since the term first emerged in 2017. By examining the historical context and the limitations of existing definitions, we have proposed a comprehensive definition that captures the essential aspects of Deepfakes: their scope, creation process, and potential for harm.

As AI continues to advance and synthetic media becomes more sophisticated, it is crucial that we have a clear and unified understanding of what constitutes a Deepfake. Only by establishing this common ground can we effectively address the challenges posed by this technology and harness its potential for positive applications while mitigating the risks of misuse.

by Ryan Ofman, Head of Science Communications and ML Engineer at Deep Media