What it is
Deepfakes are created using deep learning techniques, primarily generative adversarial networks (GANs) or autoencoders, to manipulate or generate realistic images, audio, or video. The AI model learns patterns from existing media of a target individual and then applies those patterns to new content, making it appear as if the target person is saying or doing something they never did.
The rise of deepfakes raises concerns about misinformation, fraud, and reputational damage. Their detection and proliferation are frequent topics in news and policy discussions, particularly concerning elections and cybersecurity. Companies developing AI for content authentication or deepfake detection may see increased demand. Regulatory bodies are exploring ways to combat malicious deepfake use, impacting content platforms.
Why it matters
Deepfakes blur the lines between reality and fiction, posing risks to trust, information integrity, and individual privacy, which can impact markets and policy.
Reviewed under editorial standardsUpdated September 26, 2026Not investment advice