How I built the world’s most efficient deepfake detector with $100
Conference (BEGINNER level)
Room 4
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The match becomes increasingly accurate as the similarity score approaches zero. is an ultra-realistic face generator based on neural networks, frequently used by botnets to create realistic fake profiles on the internet.

At a time when disinformation and manipulation of opinion spread through social networks, it is necessary to be equipped to spot fake identities, especially via their profile pictures which are the hardest elements to fake. During this talk, you will discover a method to detect images generated via, with 100% reliability and even a way to know the exact time the photo was produced.

The conference will focus on a technical challenge faced during the implementation of one of the attacks, which involves TensorFlow models leveraging features of a customized ElasticSearch instance, on very limited hardware.

During this beginner-friendly conference, we'll also cover the basic machine learning concepts needed to make GANs work, and a technique to generate almost undetectable fake faces yourself.

Mathis Hammel

Mathis Hammel is a tech evangelist at CodinGame, a website specialized in mini-games to learn programming. He is a specialist and technical advisor in cybersecurity, machine learning, and algorithms.

Mathis is passionate about technical challenges such as programming competitions and Capture The Flag, and holds several titles from national and international championships.