import mona1 from “./mona1.mp4”
I am a fan of the Augsburger Puppenkiste. Puppets are brought to live by pulling strings. If you ever get to Augsburg, do not miss to see a show!
Today a came across an excellent article 1, which creepily reminded me of these happy String Puppets.
I decided to hit two birds with one stone: Learn something about the current state of Deep Fakes and try out Google’s Colab environment.
Take a static picture of a person and bring it to life with a driver video which you generate. The video drives the static picture, i.e. it pulls the strings of the puppet (picture).
Here are my results with taking innocent Mona Lisa as input and driving her expressions with my grimaces:
My experiment is a very simple one, it only involves couple of seconds of face expression. No sound, no surrounding.
But it can be much more involved:
Other missing pieces like sound, background, etc. are around the corner:
It is still early days and composing various elements convincingly into a comprehensive deception requires skills, time and resources. But the barriers are vanishing by the day.
Google’s Colab is generous offer for technology consumption. It democratizes machine learning and provides everybody access to sophisticated machine learning technology.
However, I struggled to create a notebook efficiently with the speed I am used to.
Iteration cycles are slow because of waiting times and latency. Everything is run in the browser and via the Internet. A local Jupyter notebook is much more responsive. But even development speed with a local notebook pales against the cylce times you can achieve with a professional Python IDE and direct access to the command line.
Granted, setting up GPU powered machine learning is a chore!
However, pulling through it provides understanding of the capabilities and the limitations of your infrastructure. Knowing your constraints is a feature of a professional. Sooner or later you will hit a wall and then good luck trying to break through with a care-free but opaque package like Colab.
The time to set up a bespoke environment is measured in days. The speed gain you will experience in your daily work with a bespoke environment will quickly overcompensate.
Pictures are common knowledge to be easy to forge, but now video and sound is following suit. Deep Fake has become a commodity.
It opens new dimensions for phishing attacks:
- Was it really your boss calling you?
- A video from your husband in a compromising situation?
The balance has shifted irrevocably. It is already much more expensive to correct lies and allegations than to produce them credibly.
People cannot digest these changes quick enough and adapt behaviour.
We tend to believe what we see with our own eyes. We are trustful creatures. We do not accept the fact that everything which reaches our eyeballs via electronic media might be compromised.
This experiment cost me a couple of hours. Imagine a player with dedicated motivation and substantial means to produce fake news based on these technologies.
Could you spot it?
CGI generated movies from Hollywood used to be prohibitively expensive to only be available to the best funded operators, a.k.a. states. To manipulate the world into the Irak war took state department resources and an army of intelligence services.
Today, everybody can fabricate facts. You cannot trust anything you see on an electronic device! Anybody could pull the strings.
It runs against our nature. This makes it hard.
#python #machine learning #society #ai