Unit 10 - Deep Learning in Action

For this activity, I researched an impactful deep learning application: AI-generated synthetic media (deepfakes).

Overview of the Technology

Deepfakes are synthetic media generated using deep learning models, particularly Generative Adversarial Networks (GANs). These technologies can create highly realistic audio, video, or images of people saying or doing things they never actually did. While initially developed for entertainment and visual effects, the technology has seen rapid adoption across domains.

How It Works

Deepfakes rely on GANs, where two neural networks, a generator and a discriminator, are trained simultaneously. The generator creates fake content while the discriminator evaluates its authenticity. Through iterative competition, the system produces highly convincing forgeries. Modern architectures can synthesise facial expressions, voice patterns, and even lip movements with remarkable accuracy.

Socio-Technical Impacts

The potential impacts of deepfakes are both promising and deeply concerning:

• Ethics: Deepfakes can be used maliciously for misinformation, harassment, or impersonation, raising serious concerns about digital trust and consent.
• Privacy: The ability to recreate a person’s likeness without consent challenges existing frameworks for data protection and personal rights.
• Security: Deepfakes pose risks to national security, electoral integrity, and public trust in digital media.
• Social Good: On the positive side, deepfake technology has applications in accessibility (e.g., generating speech for the voiceless), film restoration, and personalised learning content.