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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."
Will this work on Japanese porn too? My friend wants to know.
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."
And please, when you cover the information with black bars, use Adobe Acrobat. (this solution brought to you by the CIA)
Push Button, Receive Bacon
Squinting your eyes also works.
damn right. I see this happening on CSI all the time, the licence plate, blurred, reflected in a window, with someone standing in front of it.. just 'clean up the image', and bobs your uncle - one licence plate revealed clear as day. :)
the problem is more the fact that so many people on the internet use just a simple mosaic to do blurring. i can cite enough examples from google image search if i wanted to. others resort to applying a motion blur effect just once which can be reversed by deconvolution if it's not blurred enough. if you use the smudge tool, good for you, i don't think there's a good way to reverse that. the problem is that blurring and mosaic techniques are simple, consistent transformations, while smudging is not.
An unclassified report was released with information blacked out to make it unclassified. The problem is that whatever software was used to produce the PDF with classified information hidden had only applied a layer which was easily removed.
People who do not understand the technology they are working with should not have this kind of release authority. And that's the hard part--the higher up you are in the food chain, the less likely you are to understand the new tools your organization is working with.
There are very few users in government who could not do their jobs just fine using Windows 3.11, WordStar 3.x and an e-mail client on a fast but simple machine.
Slaved as the government is to Microsoft's development cycle, however, the government will always be at the cutting edge of compromised.
Don't trust anyone under thirty.
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.
The whole point of the article is that blurring and pixelating beyond recognition isn't enough. You don't need to see the original numbers, you just have to find numbers that blur to a similar blob. It's a dictionary attack with blur as a hash function.
This is a kind of maximum entropy method, like the unsharp mask in image processing. Basically, if you know the blurring (convolving) function, you can reverse it. There are more sophisticated algorithms for cases where the blurring function is unknown, based on certain regularities; for example motion blur has a fixed direction and magnitude.
Escher was the first MC and Giger invented the HR department.
In the real world, data is imperfect and noisy, so the article is thus far correct. What is not correct is simply to pick the data with the nearest match, because it's a best match to the noise also. Maximum entropy is one algorithm which gives you a probabilistic answer, i.e. "the chances that this particular combination is the right one is [whatever] percent". You then pick the most likely one. Astronomers use this technique all the time for removing the blur and diffraction on their images. I personally use it regularly for nuclear spectroscopy, and it's absolutely solid if you use it carefully.
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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.