How I detect adult content in my Quantum Encryption software.

QuantumExpress Encryption system

When you’re building an encryption system like QuantumExpress, where users can encrypt anything — from confidential files to personal photos — you eventually run into a philosophical and ethical question:

Should I allow the encryption of adult or explicit content? At first, I thought: that's the user's responsibility. But then I realized... if you're developing a cryptographic product that might be distributed widely — possibly used in schools, companies, or even defense — it’s your responsibility to add ethical safeguards.

So I set out to build a lightweight, local AI filter to scan and classify images before encryption.

Problem: I Don’t Want QuantumExpress to Be Misused

I wanted a built-in mechanism that:

First of all, flags images with nudity or adult content and keeps creepy folks out of the action.

Also, it allows normal photos, even gym or beach shots and i wanted it to runs locally, with no external API call. And yes, it can be easily embedded in QuantumExpress Light, my offline Windows cryptographic platform

Solution: CLIP-Based NSFW Pre-Checker

After experimenting with some pretrained models, I settled on OpenAI’s CLIP (Contrastive Language-Image Pretraining) — an open-source model that can associate text labels with images based on semantic similarity. This model been trained on millions of images and very sensitive, yet smart in detecting what needs to be detected-porn.

What I Built:

A Python script that does the job and will integrated into QuantumExpress.

Input: Local image file (e.g., gym_guy.jpg)

Compared the image to labels like:

"man in gym shorts", "naked man", "pornographic", "nudity", "athlete", etc.

Output: Similarity scores for each label

Decision: If the highest match is NSFW, reject; else, proceed to encrypt

Real Test Example

Input image: Myself deadlifting in my garage gym, from behind, wearing gym shorts.

AI Response:

Top match: "man in gym shorts deadlifting" — 96.58%

NSFW labels: 0.00% to 0.01% Safe to encrypt

Perfect! Even though the image has skin and muscle definition, it didn’t get flagged — and that’s the subtlety I wanted. The model understood the context.

How This Compares to OpenAI's Image Generator

OpenAI’s DALL·E and image-generation systems have built-in NSFW filters — but they operate at the API level. You don’t get visibility into the decision process. It just says:

“Your prompt violates our content policy.”

For my system, I wanted:

1. Transparency

2. Full local control

3. Debuggability (you can see why it flags an image)

And now I have it.

The Plan: Integrate into QuantumExpress. This NSFW pre-checker will become a pre-encryption filter inside QuantumExpress. It won’t block the file — it’ll just give the user a decision point:

This image appears to contain adult content.

Do you still want to encrypt it?

[ Cancel ]

If you're building tools for privacy, security, or encryption, you are also building tools that can be abused. I’m not trying to play content cop — but I am trying to build ethical defaults into everything I ship. That’s what QuantumExpress stands for. And if you're an AI dev, crypto dev, or hacker like me working late in your garage gym... I hope this inspires you to do the same.