You have already practised spotting when an image, clip or soundtrack has been altered, and what that change does to a viewer. Today the question gets harder: can we still tell when media was never captured from the real world at all?
You will learn what deepfakes and synthetic media are, try a class vote detection challenge with three possible calls (real, likely synthetic, or cannot tell), then discuss what that means for news, evidence and trust.
Synthetic media is image, audio or video made wholly or partly by a machine rather than captured straight from the real world. A deepfake is a form of synthetic or heavily AI-manipulated media that makes a person appear to say or do something they did not.
Today's shared verdicts:
Clean quality is not proof something is real. Knowing when to pause and say cannot tell is part of the skill.
This is a class vote challenge. Work through each item together. For every piece of invented media, look closely with a partner: what do you notice? Then commit as a class to one verdict: real, likely synthetic, or cannot tell, and be ready to say why. After the class commits, the reasoning is revealed. The learning is in the reasoning, not in a score.
We will work four items as a class; one extra if time. Use the interactive activity on the board. Be ready to say which detail did the most work for you, and when you chose cannot tell on purpose.
In pairs, then as a class, take on this open question. There is no single correct answer the lesson is steering you toward.
When any image, voice or video could be fabricated, what changes for evidence, and for trust between people?
Use the two case cards below. For each, name one risk and one habit that would help. Be ready to share one sentence with the room.
Before you finish, check:
Pull the threads together. Deepfakes and synthetic media mean some images, voices and videos are machine-made or machine-reshaped.
Clean quality is not proof of reality, and “cannot tell” is sometimes the honest, skilled call.
The habit that changed in the room today:
People, platforms and researchers are still building better labelling and detection tools. Your part is not panic, it is careful judgment.
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