I’m excited to share my latest blog post, published on Hornetsecurity’s website, where I explore the challenges of detecting duplicate and near-duplicate images in cybersecurity. Attackers often tweak phishing images to evade detection, making traditional filtering methods less effective.
In this article, I break down hash-based techniques and color histograms, two key approaches to spotting manipulated images. These methods lay the groundwork for more advanced detection strategies to counter evolving threats.
If you’re interested in the broader topic, the first part of this series was written by Sebastien Goutal: you can check it out here.
Why it matters
Phishing kits reuse the same visuals over and over: a bank logo, a fake login screen, a delivery notice. Attackers know that blocking an exact copy is trivial, so they crop, resize, recolor or re-compress the image just enough to change every byte. Exact matching misses all of these variants. Perceptual hashes and color histograms answer a better question: does this image look like one we have already seen? They are cheap enough to run on every image in every email, which makes them the first line of defense before heavier models kick in.
Read my full article on Hornetsecurity’s website: Detection of Cyberthreats with Computer Vision (Part 2).
Let me know your thoughts, and stay tuned for more insights on image analysis in cybersecurity!
