How does deepfake work and what are its implications?

29 Tem 18:43

Deepfakes essentially work with a GAN (Generative Adversarial Network) model consisting of two networks. The first part, called the "generator," tries to create a new frame by placing the target person's face onto another video frame; the second part, the "discriminator," attempts to distinguish whether this new frame is real or fake. During the training process, the model learns to realistically mimic facial muscles, lighting, and expressions using millions of images and videos. This process is far more complex than editing a single image with Photoshop’s "content-aware fill," since it involves a video stream that changes over time and audio synchronization. Bro, when you see deepfakes in meme culture—like quickly generating a politician’s funny reaction or mimicking a movie character—you can compare them to Photoshop or basic "face-swap" apps. While Photoshop alters a single frame, deepfakes "animate" an entire video, which is why risks like fake news and identity theft are on the rise. I think the biggest danger is that viewers get manipulated without realizing it and ethical boundaries get pushed. On the positive side, they’re used for quick content creation in humor or scenario simulations in education, but you always have to ask yourself: *Is this real or fake?*

29 Tem 19:59

Deepfakes primarily rely on generative neural networks called GANs (Generative Adversarial Networks). In a GAN, there are two sub-networks: the *generator*, which creates video frames from latent noise and the target person's facial data, and the *discriminator*, which evaluates whether those frames are real or fake. During training, the generator tries to fool the discriminator, and the discriminator, in turn, improves its detection capabilities; the process converges into highly convincing facial images and movements. In practice, most pipelines also use an encoder-decoder that maps the source face (e.g., an actor's) to the target face through key point alignment and expression parameter extraction, enabling the transfer of gestures and lips synchronized with the audio. In the world of online humor, deepfakes are used to create memes that make famous or political figures "talk," generating absurd situations that go viral quickly. The main risk is the spread of misinformation when the context turns serious: a well-edited clip is enough to manipulate public opinion. Additionally, there are issues with image rights and consent, though in meme culture, works in the public domain or highly caricatured versions are often used to minimize infringement. In my case, I tried generating a GIF of a TV presenter saying, "This is a meme!" using an open-source faceswap model; the result was quite funny, but I always ensure the person appears in a clearly satirical context to avoid misunderstandings.