Likeness Detection

Extracted Attributes

• Developer

  YouTube

• Target Content

  AI-generated/edited media (deepfakes)

• Detection Scope

  Simulated faces, voices

• Primary Purpose

  Protect creators' likeness rights

• Risks Mitigated

  Child sexual abuse material, revenge porn, fake news, hoaxes, bullying, financial fraud, disinformation, hate speech, election interference

• Blueprint/Foundation

  Content ID system

• Supported Legislation

  NO FAKES ACT

• Underlying Technologies (Deepfakes)

  Machine learning, Artificial intelligence, Facial recognition algorithms, Neural networks (VAEs, GANs)

Timeline

• YouTube introduced its likeness detection system in partnership with the Creative Artists Agency (CAA). (Source: Web Search Results)

  2024-12

• YouTube explained how the likeness detection program works, detailing its ability to automatically detect simulated faces or voices made with AI tools. (Source: Web Search Results (interpreted 'earlier this year' from an April 2025 article))

  2025-01-01

• YouTube announced an expansion of its pilot program for likeness detection, designed to identify and manage AI-generated content featuring the likeness of creators, artists, and influential figures. (Source: Web Search Results)

  2025-04-09

Deepfake

Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, including facial recognition algorithms and artificial neural networks such as variational autoencoders (VAEs) and generative adversarial networks (GANs). In turn, the field of image forensics has worked to develop techniques to detect manipulated images. Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections. In response, the information technology industry and governments have proposed recommendations and methods to detect and mitigate their use. Academic research has also delved deeper into the factors driving deepfake engagement online as well as potential countermeasures to malicious application of deepfakes. From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing for the disruption of the entertainment and media industries.

Web Search Results

• Likeness Detection API - Hive AI

  ###### Our deep learning model compares visual content holistically, not pixel-by-pixel. We detect likeness based on the defining characteristics of well-known characters and artworks. #### Comprehensive search index Regularly updated, our Likeness Detection model detects a comprehensive suite of the most well-known characters across the most popular IP domains. [...] Regularly updated, our Likeness Detection model detects a comprehensive suite of the most well-known characters across the most popular IP domains. ### Comprehensive search index Regularly updated, our Likeness Detection model detects a comprehensive suite of the most well-known characters across the most popular IP domains. Regularly updated, our Likeness Detection model detects a comprehensive suite of the most well-known characters across the most popular IP domains. [...] Identifies the “likeness” of the most popular characters and artworks in images across a constantly expanding breadth of well-known IP domains. # Likeness Detection API ###### Identifies the “likeness” of the most popular characters and artworks in images across a constantly expanding breadth of well-known IP domains. ## Comprehensive searches, actionable results ### Comprehensive searches, actionable results

• YouTube expands its 'likeness' detection technology ... - TechCrunch

  The company introduced its likeness detection system in partnership with the Creative Artists Agency (CAA) in December 2024. The new technology builds on YouTube’s efforts with its existing Content ID system, which detects copyright-protected material in users’ uploaded videos. Similar to Content ID, the program works to automatically detect violating content — in this case, simulated faces or voices that were made with AI tools, YouTube explained earlier this year. Techcrunch event [...] YouTube on Wednesday announced an expansion of its pilot program designed to identify and manage AI-generated content that features the “likeness,” including the face, of creators, artists, and other famous or influential figures. The company is also publicly declaring its support for the legislation known as the NO FAKES ACT%20and%20other%20technologies.), which aims to tackle the problem of AI-generated replicas that simulate someone’s image or voice to mislead others and create harmful [...] In addition to the likeness detection technology pilot, the company also previously updated its privacy process to allow people to request the removal of altered or synthetic content that simulates their likeness. It also added likeness management tools that let people detect and manage how AI is used to depict them on YouTube. Topics AI, deepfakes, Media & Entertainment, YouTube Sarah Perez Consumer News Editor

• Guide to Deepfake Detection - Paravision

  Any of various media, esp. a video that has been digitally manipulated to replace one person’s likeness convincingly with that of another, often used maliciously to show someone doing something that he or she did not do.¹ [...] Deepfakes raise profound concerns regarding human rights violations and infringements on personal privacy, mainly through the unauthorized use of individuals’ likenesses in fabricated content. The proliferation of deepfake-generated pornographic material disproportionately affects women and can have devastating consequences for victims. Studies indicate that 96% of deepfake videos on the internet are pornographic, highlighting the prevalence of non-consensual and malicious use of deepfake

• Advanced Deepfake Detection: Essential Guide from Jumio

  Deepfakes are highly realistic, digitally manipulated videos or images where a person's likeness, voice, or actions are artificially created or altered using deep learning techniques, especially with neural networks. These tools can generate highly convincing media that appears real but is, in fact, synthetic. Deepfakes often use techniques like generative adversarial networks (GANs) to blend and map one person’s facial expressions or voice onto another, creating the illusion that the subject [...] Liveness detection is a technique used by identity verification solution providers to ensure that the biometric sample (such as a fingerprint, face or voice) being presented is from a live person rather than a static image, video or other spoofing method (e.g., a deepfake). It is a crucial security feature in online verification and authentication solutions to prevent spoofing attacks, where someone might try to create an online account or gain unauthorized access by presenting a photo, mask or [...] other fake representation of a person’s biometric trait.

• Liveness Detection: Enhancing Security with Biometrics - Aware, Inc.

  detection and how it works to help safeguard digital identities. [...] Human Resources Healthcare Law Enforcement Physical Access Control Thought Leadership ENTERPRISE SECURITY [...] ## What is Liveness Detection? Liveness detection is any technique used to detect a spoof attempt by determining whether the source of a biometric sample is a live human being or a fake representation. This is done using algorithms that analyze data collected from biometric sensors to determine whether the source is live or reproduced. ### There are two main categories of liveness detection: