In the rapidly evolving landscape of artificial intelligence, deepfake technology has emerged as a double-edged sword. These synthetic media creations, generated through advanced deep learning techniques, have the power to blur the lines between reality and fiction.
As deepfakes become more prevalent online — and increasingly more advanced — it is evident that this technology poses both fascinating opportunities and grave concerns for businesses and individuals.
What is a Deepfake?
A deepfake is a piece of synthetic media created with AI models trained in deep learning methods. To put it more simply, a deepfake is an image, video, audio, or text that appears authentic but is decidedly not. In many cases, deepfakes alter existing content, swapping out faces, landmarks, or other objects with AI-generated substitutes. In other instances, deepfake content is created from scratch, depicting events or conversations that never occurred.
It is important to recognize that machine deep learning and artificial intelligence are essential in creating a deepfake. If an image, video, or audio is created or altered through more conventional methods, such as computer software like Photoshop (without leveraging its AI features) or traditional CGI methods in Hollywood, it is not considered a deepfake.
Deepfakes rely on computer systems modeled loosely on the neural structure of the human brain to recognize patterns in data. The process of developing a deepfake usually consists of feeding hundreds or thousands of data points (images, video and audio samples, vast amounts of text) into the deep learning network, training it to reconstruct visual, audio, and textual patterns.
Deepfakes can be created using different underlying AI technologies. They are typically generated by specialized algorithms and applications that “learn” the information being synthesized: the appearance and motion of someone’s face in images or videos, the smallest intricacies of someone’s voice, and syntactical patterns in someone’s writing style. Subsequently, creators use these deep learning AI models to superimpose this information onto existing images, footage or audio clips, or to create entirely new media and text.
The Dangers of Deepfakes
While there are legitimate, even beneficial uses for deepfakes in many areas of human life, there is great danger in this technology being used for nefarious purposes. An overwhelming majority of current deepfake online content is deepfake p*rnography.
Deepfakes are also often used to spread disinformation, erode public trust, and overcome security measures to clear the path for fraudsters and cybercriminals to launch attacks on individuals, businesses, the media, and government and public institutions.
What are Image-Based Deepfakes?
Deepfake images refer to synthetically generated images — photographs, paintings, and other mediums — that were created or manipulated using artificial intelligence technology, specifically deep learning methods.
As photographs are essential in news reporting to illustrate facts and events being reported, the ability of deepfakes to convincingly alter real images to show anything their creator desires is a serious threat to public and individual perception of what is real or factual. While deepfake images can be used in positive contexts such as art and entertainment, the potential for malicious misuse is staggering: deepfake images can be used to sow disinformation, defame public figures, interfere with the democratic process, enable fraudulent advertising, phishing, and fraud, and steal from artists.
What are Video Deepfakes?
Deepfake video refers to synthetically generated videos that were created or altered using artificial intelligence technology, specifically deep learning methods. The creation of a deepfake video typically involves training a deep neural network on a large dataset of videos and images featuring the target individuals. The model learns their facial features, expressions, and mannerisms, enabling it to generate new video content that looks authentic.
Deepfake videos have gained attention due to both their potential in the entertainment industry and their malicious misuse. The current discourse about the dangers of deepfakes is mostly focused on video, as this form is the most widely consumed type of media around the world. Video deepfakes are deployed to spread disinformation, mislead the public, erode trust in public institutions, and malign individuals. The overwhelming majority of deepfakes created and deployed online fall under the category of deepfake p*rnography, a heinous abuse of generative AI technology that violates basic principles of consent and privacy and propagates violence and abuse against women.
Video deepfakes have also been deployed to manipulate free elections, impersonate public figures, and change public opinions on crucial issues. Cybercriminals utilize deepfake videos to impersonate and blackmail individuals and to bypass biometric security measures utilized by major companies.
What is Deepfake Audio?
Deepfake audio refers to synthetically generated sound that has been created or altered using artificial intelligence technology, specifically deep learning methods.
The most common example of deepfake audio is voice cloning, involving synthetically generated human speech of people saying things they never actually said. Audio deepfakes are already being deployed in disinformation attacks meant to manipulate free elections, mislead the public about important issues at home and abroad, misrepresent the validity of legitimate news, and defame individuals.
Voice cloning is also a particular concern in industries that use voice biometric verification to access customer account information and money, most notably in the banking industry. Many companies that receive a high volume of phone calls as part of their business report constant deepfake attacks on their infrastructure via voice cloning efforts.
Audio deepfake models are trained on target audio samples. The quality of the deepfake depends on the amount of audio samples used; however, the newest tools available for public use can create convincing speech deepfakes with only 30 seconds of audio.
The rise of deepfakes has ushered in a new era of synthetic media, one that challenges our traditional notions of trust and authenticity. While the potential applications of this technology are vast, ranging from entertainment to artistic expression, the risks of misuse cannot be overlooked.
As we navigate this uncharted territory, it is crucial to remain vigilant, foster ethical practices, and develop robust detection and mitigation strategies. Only through a collective effort can we harness the power of deepfakes and generative artificial intelligence for good while safeguarding against their malicious exploitation.
