Explanatory Note on Deepfake Technology under Turkey’s KVKK

Executive Summary

The Personal Data Protection Authority has published an explanatory note on deepfake technology—digital media created through artificial intelligence that manipulates a person’s face, voice, or actions to appear real. While deepfakes can be used for creative and legitimate purposes such as cinema or art, they also pose serious risks to personal data, reputation, and social trust. The Authority warns that these manipulated contents often rely on personal data collected from social media and can lead to privacy violations, fraud, or harassment. The note calls for greater public awareness, technical safeguards, and responsible use of AI technologies in compliance with Turkey’s data protection law.

At a Glance

• Deepfake combines “deep learning” and “fake,” referring to AI-generated, realistic but fabricated media.

• Can serve creative uses but also enable identity theft, misinformation, and reputational harm.

• Relies on processing biometric and other personal data, often without consent.

• Users should limit sharing of face and voice data on social media.

• Organisations should implement detection tools and cybersecurity measures.

• Development of anti-deepfake technologies and awareness campaigns is strongly encouraged.

  
  

Context & Background

In April 2025, the Personal Data Protection Authority released an explanatory note titled “Deepfake” to inform the public about the personal data risks arising from AI-generated synthetic media.

Deepfakes—short for “deep learning” plus “fake”—are videos, images, or audio recordings produced by artificial intelligence to convincingly replicate real people’s likeness or voices. Although such technology can enhance film production or digital art, it also enables highly realistic misinformation, impersonation, and blackmail.

The Authority highlights that the danger of deepfakes lies in their dependence on personal data—especially facial images, voice samples, and movement data—collected, combined, and manipulated to fabricate synthetic but convincing content. This manipulation not only undermines privacy but can also constitute a violation of the Personal Data Protection Law No. 6698 when performed without consent or lawful basis.

Key Points Explained

1. What Is Deepfake?

Deepfake technology uses deep learning algorithms to synthesise or alter human likeness and voice. By analysing and training on existing photos, videos, or audio clips, these systems can produce entirely new media that mimic real individuals with striking realism. Even a small number of recordings or images can suffice to generate convincing fake content. As a result, individuals’ faces, gestures, or voices can be digitally replicated or manipulated without their knowledge, raising serious ethical and legal concerns.

2. Uses and Applications

Deepfakes can serve legitimate purposes in:

• Film and entertainment, such as recreating historical figures or enhancing visual effects.

• Advertising and media, where likenesses may be used to personalise content.

  
  

However, the same technology can also be abused for:

• Defamation or reputational damage,

• Political or financial manipulation,

• Impersonation and fraud, and

• Harassment and blackmail.

  
  

By enabling highly realistic falsification of identity, deepfakes blur the line between real and fabricated information, threatening public trust and individual dignity.

3. How Deepfakes Threaten Privacy

The Authority identifies deepfake content as a serious privacy risk because it manipulates personal data—specifically biometric data such as facial features and voice patterns. These manipulations can result in the creation of hybrid data: artificial content that still contains elements of real personal data. When shared or published, such content can mislead viewers and cause emotional, reputational, or even financial harm.

Vulnerable groups, particularly children and the elderly, are especially at risk of exploitation through deceptive deepfake materials. Attackers may use such content for blackmail or to coerce victims into sharing further information or funds.

4. Detecting Deepfakes

The explanatory note lists several practical indicators to help users identify deepfake content:

• Unnatural eye movements or lack of blinking.

• Mismatch between facial expression and spoken emotion.

• Inconsistent reflections in glasses or lighting changes.

• Discrepancies between head and body movement.

• Unrealistic hair or blurred facial features.

• Visible anomalies when the video is slowed down.

• Robotic or monotone speech patterns.

• Illogical or contextually wrong responses in video interactions.

  
  

These clues, combined with digital forensics and dedicated detection software, can help individuals and organisations verify authenticity before trusting or sharing online content.

5. How to Protect Against Deepfake Risks

The Authority emphasises that preventing the misuse of deepfake technology starts with individual vigilance. People should avoid oversharing visual or audio content—particularly on social media, which serves as the main source of training data for deepfake systems.

Beyond personal caution, both technical and organisational measures are necessary:

• Raising awareness: Educate the public about deepfake risks and signs of falsification.

• Detection tools: Use AI-powered applications to identify manipulated content.

• Usage control: Restrict and monitor who can access or distribute deepfake software.

• Institutional measures:

  • Strengthen cybersecurity and network monitoring.

  • Establish internal reporting and incident response channels.

  • Ensure coordination between IT and public relations teams in case of deepfake-related incidents.

• Technological countermeasures: Encourage the development of anti-deepfake software that identifies and blocks synthetic identity theft on social media or content platforms.

• Industry involvement: Cybersecurity firms should develop and maintain databases linking original media with their authentic references to support verification.

  
  

By combining public awareness with robust technological solutions, the risks of misinformation and identity abuse can be significantly reduced.

Conclusion

The Authority’s explanatory note on deepfakes serves as a reminder that technological innovation and data protection must evolve together. While deep learning techniques offer creative and practical opportunities, they also open the door to manipulation, misinformation, and privacy violations on an unprecedented scale. Protecting individuals from these threats requires shared responsibility: users must act with awareness, institutions must enforce strong data governance, and technology providers must embed privacy and security principles into every stage of development. Only through a collective and informed approach can society benefit from artificial intelligence without compromising the fundamental right to personal data protection.

Source: Based on the explanatory note “Deepfake Bilgi Notu” published by the Personal Data Protection Authority (April 2025).