Apple wants to help protect children from people who use communication tools to recruit and exploit them, and limit the spread of CSAM files. On the other side, Apple's plan has been particularly controversial and has prompted concerns about the system potentially being abused by governments as a form of mass surveillance. But rather than analyzing the benefits and drawbacks of this new feature, I would like to say a few words about the cryptographic techniques and protocols used for this system implementation. Before explaining these technologies, let's step back for a moment and take a quick look at the whole process of CSAM detection and its steps to get some more context around this. NeuralHash is a perceptual hashing function that maps images to numbers. The system computes these hashes by using an embedding network to produce image descriptors and then converting those descriptors to integers using a Hyperplane LSH (Locality Sensitivity Hashing) process.

Today's blog post is on image hashing -- and it's the hardest blog post I've ever had to write. Image hashing isn't a particularly hard technique (in fact, it's one of the easiest algorithms I've taught here on the PyImageSearch blog). But the subject matter and underlying reason of why I'm covering image hashing today of all days nearly tear my heart out to discuss. The remaining introduction to this blog post is very personal and covers events that happened in my life five years ago, nearly to this very day. If you want to skip the personal discussion and jump immediately to the image hashing content, I won't judge -- the point of PyImageSearch is to be a computer vision blog after all. To skip to the computer vision content, just scroll to the "Image Hashing with OpenCV and Python" section where I dive into the algorithm and implementation. But while PyImageSearch is a computer vision and deep learning blog, I am a very real human that writes it. And sometimes the humanity inside me needs a place to share.