I have a small question that I think is worth discussing. We all know that the technology of dipfakes is developing rapidly, and every year they become more and more realistic. Today, it's already hard to tell a fake video or audio from a real one by eye. But along with this, the methods of their detection are also developing. The question is, what technologies are available now to fight dipfakes?

There are algorithms that analyze eye blinks - dipfakes often have errors in blink rate. Some popular tools include Deepware Scanner and Sensity AI, which help find fake videos. Another way is digital watermarks, which large companies such as Microsoft and Adobe have started to implement in their programs so that the authenticity of content can be verified. On a deeper level, neural network models trained on original and fake data help find differences that are imperceptible to the human eye. I recently came across an interesting article on this topic - https://signalscv.com/2025/02/the-ri...rtion-schemes/. It tells about cases when dipfeaks are used in fraud and how they are combated. In general, the technology for detecting dipfakes is developing, but dipfakes themselves are becoming more and more complex.

To be honest, I haven't really delved into this problem, but I realize that with the development of AI, such fakes will only become more qualitative. It's good that there are ways to fight back, but I think it's still hard for the average user to protect themselves from such fakes without special tools.