I’m happy to share that Part Three of my blog series on image analysis in cybersecurity is now live on Hornetsecurity’s website!
In this latest article, I explore advanced techniques for detecting near-duplicate images, which go beyond hash-based methods and color histograms. As attackers grow more sophisticated, modifying visual content just enough to evade detection, our defenses must evolve too.
This post dives into content-based approaches, including object recognition, embedded text comparison, and hybrid techniques, to help security systems spot even the most cleverly disguised threats.
If you missed it, Part Two covered foundational techniques like hash matching and histogram analysis. You can read it here.
Why it matters
Hashes and histograms compare pixels; attackers have learned to change pixels. Shift a layout, swap a background, or rewrite a few words of the lure, and a pixel-level fingerprint no longer matches, even though a human would instantly see the same scam. Content-based approaches compare what the image shows: which objects are present, what text it carries, and how they are arranged. They cost more to compute, which is why they work best combined with the cheaper techniques from Part Two: the fast methods filter the bulk of the traffic, and the content-based ones catch the variants built to slip past them.
Check out the full article: Detection of Cyberthreats with Computer Vision (Part 3).
As always, I’d love to hear your feedback, and stay tuned for more on computer vision in cybersecurity!
