Blurring Images Not So Secure

An anonymous reader writes "Dheera Venkatraman explains in a webpage how an attacker might be able to extract personal information such as check or credit card numbers, from images blurred with a mosaic effect, potentially exposing the data behind hundreds of images of blurred checks found online, and provides a ficticious example. While much needs to be developed to apply such an algorithm to real photographic images, he offers a simple, yet obvious solution: cover up the sensitive information, don't blur it."

Comments

Summary of technique

[pla]

He basically points out that a blurred mosaic amounts to a form of inexact hash function. While irreversable, if you have a small enough input space, you can exhaustively hash all possible candidates and pick the one(s) that best match the target.

Interestingly enough, while he points out that most financial account numbers contain a degree of error detection and correction, he chooses to use that to reduce the match set, rather than the candidate set. I suppose this would matter if you wanted to prove a hypothesis (if the best match yields a valid number, you have a p=[valid/total]), but if you just want to steal someone's account info, you'd do better to reduce your processing time and just try the best few results in order.

You can actually go one step further with wavelets

[StandardCell]

In a lot of advanced image processing where you want to upscale an image, you can actually use a wavelet-based scaling technique that recovers amazing amounts of detail. In most digital TVs these days, they use a two-dimensional polyphase finite impulse response filter tuned for a certain degree of Gibbs phenomenon (ringing around harder edges) versus detail loss. But this has its limits, and it doesn't intelligently reconstruct the image details. In addition, it's notoriously difficult to tune properly for all content.

In contrast, wavelet based scaling can actually reconstruct phenomenal amounts of detail from a degraded image. For digital TV applications where you have DVDs or standard definition content displayed on a high-definition fixed-resolution display, wavelet-based scaling can actually make real details re-emerge where they weren't there before. The bottom line explanation is understanding and interpreting the influence of adjacent pixels with a minimum of error as the article's author demonstrates (although, as the parent post explains, he's going about it in a convoluted way). I've actually seen the preliminary results that some engineers had shown me that makes it look like something a government agency would use to enhance satellite or surveillance camera images. It makes DVDs look almost exactly like HD-DVD or Blu-Ray HD content. In fact, I expressed my concern that this scaling method could be used on digital TVs to actually "unmask" blurred or blocked faces on TV shows and introduce liability issues.

Nevertheless, it is possible to reconstruct a LOT of detail from blocked out or blurred faces or pretty much any content. Doing it in real time on HD resolution displays is a different matter altogether as it requires enormous computing power. But it is coming in the next 3-5 years. If you're really interesting in blocking out content on digital photos, use a solid black color over the part you don't want recognized.