Over the last few months, I’ve been exploring how far AI can take us, not just in theory, but in making them real. Could we build real tools using only basic computers and AI? Could we test techniques like AI image detection, not in giant labs, but in everyday settings? This journey led me to try something specific: building an AI image detector. A tool that can help detect fake vs real images using nothing more than logic, a dataset, and a trained model. It wasn’t just about building a tool. It was about testing what AI and I could do together, and this article is the second in a series where I push these limits further. Breaking It Down with AI I had one question in mind: Can I build an image detector from scratch using a dataset of real and fake images? I turned to AI for help, and ChatGPT walked me through the entire process step by step: How to structure my dataset correctly.
How to label images for training.
What type of model to use (we chose a CNN).
How to save the model as an .h5 file for reuse.
And how to package it in a way that works even outside of Python. How to structure my dataset correctly. How to label images for training. What type of model to use (we chose a CNN). How to save the model as an .h5 file for reuse. And how to package it in a way that works even outside of Python. There were technical roadblocks. The .exe version kept failing. But with each attempt, I learnt more about how AI models work under the hood and how to make them truly portable. How Does the AI Image Detector Work? The idea behind the tool is simple. You give it an image. It tells you whether it’s real or AI-generated. Here’s how it works behind the scenes: The model was trained on two datasets: one of real images and one of AI-generated ones. Each image was resized, normalised, and fed into a convolutional neural network. After many training rounds, the model reached a reliable accuracy. I then saved it as an .h5 file, allowing it to be reused in other projects, like a web app or an executable. The result? A working prototype of an AI image detector that I can share on GitHub ai image detector and Kaggle and even integrate into a website for public use. ai image detector Why I Built It I didn’t build this tool to become the next deepfake detector of the world. I built it to test an idea: Can regular people build useful AI tools using what’s available today? Turns out: yes. And tools like this could become helpful in small but important ways: Teachers showing students how AI training works.
Developers are learning model deployment.
Journalists testing image authenticity in real time.
Anyone who wants to check a suspicious profile pic online. Teachers showing students how AI training works. Developers are learning model deployment. Journalists testing image authenticity in real time. Anyone who wants to check a suspicious profile pic online. It’s not a one-click solution. But it’s a window into what’s possible. Final Thoughts This is just the beginning. With every article and every test, I’ll go deeper. I’ll try new ideas. I’ll build and try again. Because that’s how we learn, by doing. The AI Image Detector was one test. There are many more to come. If you’re curious about what you can create with AI, start now. Don’t wait for permission. Don’t wait for perfection. You can build something real. And maybe, you’ll surprise yourself.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

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