Hi, I'm Luca a visual designer and a PhD student in design for social change at ISIA Roma. My research explores how humans use technology in intended and unintended ways.

Publications

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Notes

25 Jan 2026

I’m working on a tool to explore QR code

Update

I uploaded a corrupted PDF into an LLM to understand how it analyzes it. First, I modified the font using non-contextual alternates and then tested the file across several different models.

As expected, the results are twofold:

  1. The model uses OCR to read the text. In this case, the human reader and the machine obviously see the same thing.
  2. The model accesses the text underlying the contextual alternates, thereby reading what would have been written had the font remained unmodified.

This raises critical concerns and a series of questions. Is it possible to perform data injection through this method? What kind of dynamics are enabled between humans and machines when they are not perceiving the same information?

It is possible use the OpenType Feature to hidden messages in the text?

By manipulating a typeface through font editors such as Fontra, it is possible to leverage OpenType Features to trigger specific character substitutions where a predefined sequence of letters is replaced by a single, custom glyph. While typographers traditionally use this functionality for automatic ligatures (e.g., the ‘fi’ combination, Unicode U+FB01), this research applies the same logic to create semantic inversion.

By modifying the font’s substitution rules, specific word sequences can be rendered as their opposites: for instance, the string ‘g-o-o-d’ can be mapped to a single glyph visually representing the word ‘bad’. Consequently, while the underlying data remains ‘good’, the visual output displayed to the human reader is ‘bad’, creating a deliberate discrepancy between the encoded text and its visual perception.

While looking at a QR code, I found myself questioning its internal logic. I realized that these symbols represent a unique technological paradox: we are surrounded by them, yet we have no biological way to verify if a QR code is functional simply by looking at it.

How can we visualize the gap between human pattern recognition and algorithmic data validation? I wanted to develop a tool that forces us to confront this question—an experiment in “digital pareidolia” where the brain sees meaning where the machine finds only noise.s

So I start asking how can develop a tool that suggests this question. I did this demo where the squere are random generated and a mask of a QR code go around the screen. If you look at its it’s seems all QR Code but if you use your phone nowone to try to read it no one it is

Next step

The next phase of this study involves moving the project from the laboratory to the public sphere. By presenting this tool to an audience, I aim to document the moment of cognitive friction that occurs when a user’s primary tool for digital transition (the smartphone camera) fails to validate what their eyes have already accepted as a functional object.

I did a query that goes to the last page on Google, or at least, that was my intention. It’s not that easy because I realized it’s not that easy because Google only shows a limited number of entries. Why is that?

Where are the other results?

I read that Google can display up to 1,000 results, but in many of my searches, it stops between 250 and 350.

Publications

No publications found.