Deepfakes

What are AI-generated counterfeits?

Deepfakes are media content that is created or manipulated with the help of artificial intelligence (AI). They look deceptively real, but are in fact completely artificially created. This is made possible by deep learning, a method of machine learning.

The technology behind this is based on artificial neural networks that analyze large amounts of training data. Generative adversarial networks (GANs) are particularly widespread – a process in which two networks work against each other: A generator creates content and a discriminator evaluates its authenticity. This principle was developed by Ian Goodfellow and revolutionized the development of synthetic media.

Other architectures are also used, for example Convolutional Neural Networks (CNNs) for images or Recurrent Neural Networks (RNNs) – often in combination with Long Short-Term Memory (LSTM) – for language. Large language models and techniques from the field of natural language processing (NLP) now play an important role for text content.

Why are deepfakes risky?

Such realistic manipulations can be used specifically to deceive. It becomes particularly critical when deepfakes are used in politics, on social media platforms or in companies to spread false information or imitate people.

Deepfakes represent a growing risk in the area of cyber security. Attackers use synthetic voices, for example, to pretend to be superiors on the phone – a modern case of social engineering that can lead to massive security incidents. Sensitive information falls into the wrong hands.

Protection against potential threats is also becoming more complex, as counterfeits are becoming increasingly difficult to detect. Companies are required to expand their IT security measures.

How can you protect yourself?

The first solutions for detecting deepfakes are also based on machine learning and are sometimes used as open source tools. These analyze image or audio material for typical characteristics of artificial processing. In combination with training and clear processes, they can help to identify and avoid AI-generated content.

A strong awareness culture within the company is also important. The combination of technology, media skills and organizational measures provides long-term protection against manipulation by artificially generated content.