bellingcat.com als Alibi für westliche Medien und Regierungen?28.03.2015 um 19:58
Danke, verständlich. :D
Danke, verständlich. :D
The camera model could not be found in the exif.Also das Bild von Paris Match enthält nicht offen in den EXIF Daten das Kameramodell, auch GPS Daten sind nicht drin. Sind schon viele Daten da, aber es fehlt doch das was wirklich interessant wäre. Die Frage ist warum.
In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1634362&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1634362
No Two Digital Cameras Are the Same: Fingerprinting Via Sensor Noisehttp://33bits.org/2011/09/19/digital-camera-fingerprinting/ (Archiv-Version vom 07.04.2015)
The previous article looked at how pieces of blank paper can be uniquely identified. This article continues the fingerprinting theme to another domain, digital cameras, and ends by speculating on the possibility of applying the technique on an Internet-wide scale.
For various kinds of devices like digital cameras and RFID chips, even supposedly identical units that come out of a manufacturing plant behave slightly differently in characteristic ways, and can therefore be distinguished based on their output or behavior. How could this be? The unifying principle is this:
Microscopic physical irregularities due to natural structure and/or manufacturing defects cause observable, albeit tiny, behavioral differences.
Digital camera identification belongs to a class of techniques that exploits ‘pattern noise’ in the ‘sensor arrays’ that capture images. The same techniques can be used to fingerprint a scanner by analyzing pixel-level patterns in the images scanned by it, but that’ll be the focus of a later article.
A long-exposure dark frame. Click image to see full size. Three ‘hot pixels’ and some other sensor noise can be seen.
A photo taken in the absence of any light doesn’t look completely black; a variety of factors introduce noise. There is random noise that varies in every image, but there is also ‘pattern noise’ due to inherent structural defects or irregularities in the physical sensor array. The key property of the latter kind of noise is that it manifests the same way every image taken by the camera. Thus, the total noise vector produced by a camera is not identical between images, nor is it completely independent.
The pixel-level noise components in images taken by the same camera are correlated with each other.
Nevertheless, separating the pattern noise from random noise and the image itself — after all, a good camera will seek to minimize the strength or ‘power’ of the noise in relation to the image — is a very difficult task, and is the primary technical challenge that camera fingerprinting techniques must address.
Security vs. privacy. A quick note about the applications of camera fingerprinting. We saw in the previous article that there are security-enhancing and privacy-infringing applications of document fingerprinting. In fact, this is almost always the case with fingerprinting techniques.
Camera fingerprinting can be used on the one hand for detecting forgeries (e.g., photoshopped images), and to aid criminal investigations by determining who (or rather, which camera) might have taken a picture. On the other hand, it could potentially also be used for unmasking individuals who wish to disseminate photos anonymously online.
Sadly, most papers studying fingerprinting study only the former type of application, which is why we’ll have to speculate a bit on the privacy impact, even though the underlying math of fingerprinting is the same.
Most fingerprinting techniques have both security-enhancing and privacy-infringing applications. The underlying principles are the same but they are applied slightly differently.
Another point to note is that because of the focus on forensics, most of the work in this area so far has studied distinguishing different camera models. But there are some preliminary results on distinguishing ‘identical’ cameras, and it appears that the same techniques will work.
In more detail. Let’s look at what I think is the most well-known paper on sensor pattern noise fingerprinting, by Binghamton University researchers Jan Lukáš, Jessica Fridrich, and Miroslav Golja. Here’s how it works: the first step is to build a reference pattern of a camera from multiple known images taken from it, so that later an unsourced image can be compared against these reference patterns. The authors suggest using at least 50, but for good measure, they use 320 in their experiments. In the forensics context, the investigator probably has physical possession of the camera and therefore can generate an unlimited number of images. We’ll discuss what this requirement means in the privacy-breach context later.
There are two steps to build the reference pattern. First, for each image, a denoising filter is applied, and the denoised image is subtracted from the original to leave only the noise. Next, the noise is averaged across all the reference images — this way the random noise cancels out and leaves the pattern noise.
Comparing a new image to a reference pattern, to test if it came from that camera, is easy: extract the noise from the test image, and compare this noise pixel-by-pixel with the reference noise. The noise from the test image includes random noise, so the match won’t be close to perfect, but nevertheless the correlation between the two noise patterns will be roughly equal to the contribution of pattern noise towards the total noise in the test image. On the other hand, if the test image didn’t come from the same camera, the correlation will be close to zero.
The authors experimented with nine cameras, of which two were from the same brand and model (Olympus Camedia C765). In addition, two other cameras had the same type of sensor. There was not a single error in their 2,700 tests, including those involving the two ‘identical’ cameras — in each case, the algorithm correctly identified which of the nine cameras a given image came from. By extrapolating the correlation curves, they conservatively estimate that for a False Accept Rate of 10-3, their method achieves a False Reject Rate of anywhere between 10-2 to 10-10 or even less depending on the camera model and camera settings.
The takeaway from this seems to be that distinguishing between cameras of different models can be performed with essentially perfect accuracy. Distinguishing between cameras of the same model also seems to have very high accuracy, but it is hard to generalize because of the small sample size.
Improvements. Impressive as the above numbers are, there are at least two major ways in which this result can, and has been improved. First, the Binghamton paper is focused on a specific signal, sensor noise. But there are several stages in image acquisition and processing pipeline in the camera, each of which could leave idiosyncratic effects on the image. This paper out of Turkey incorporates many such effects by considering all patterns of certain types that occur in the lower order (least significant) bits of the image, which seems like a rather powerful technique.
The effects other than sensor noise seem to help more with identifying the camera model than the specific device, but to the extent that the former is a component of the latter, it is useful. They achieve a 97.5% accuracy among 16 test cameras — but with cellphone cameras with pictures at a resolution of just 640×480.
Second is the effect of the scene itself on the noise. Denoising transformations are not perfect — sharp boundaries look like noise. The Binghamton researchers picked their denoising filter (a wavelet transform) to minimize this problem, but a recent paper by Chang-Tsun Li claims to do it better, and shows even better numerical results: with 6 cameras (all different models), accurate (over 99%) identification for image fragments cropped to just 256 x 512.
What does this mean for privacy? I said earlier that there is a duality between security and privacy, but let’s examine the relationship in more detail. In privacy-infringing applications like mass surveillance, the algorithm need not always produce an answer, and it can occasionally be wrong when it does. The penalty for errors is much lower. On the other hand, the matching algorithm in surveillance-like applications needs to handle a far larger number of candidate cameras. The key point is:
The parameters of fingerprinting algorithms can usually be tweaked to handle a larger number of classes (i.e., devices) at the expense of accuracy.
My intuition is that state-of-the-art techniques, configured slightly differently, should allow probabilistic deanonymization from among tens of thousands of different cameras. A Flickr or Picasa profile with a few dozen images should suffice to fingerprint a camera. Combined with metadata such as location, this puts us within striking distance of Internet-scale source-camera identification from anonymous images. I really hope there will be some serious research on this question.
Finally, a word defenses. If you find yourself in a position where you wish to anonymously publicize a sensitive photograph you took, but your camera is publicly tied to your identity because you’ve previously shared pictures on social networks (and who hasn’t), how do you protect yourself?
Compressing the image is one possibility, because that destroys the ‘lower-order’ bits that fingerprinting crucially depends on. However, it would have to be way more aggressive than most camera defaults (JPEG quality factor ~60% according to one of the studies, whereas defaults are ~95%). A different strategy is rotating the image slightly in order to ‘desynchronize’ it, throwing off the fingerprint matching. An attack that defeats this will have to be much more sophisticated and will have a far higher error rate.
The deanonymization threat here is analogous to writing-style fingerprinting: there are simple defenses, albeit not foolproof, but sadly most users are unaware of the problem, let alone solutions.
LaDerobee schrieb:Ernsthaft, ich bin beeindruckt und das nicht nur da das der erste fundierte und sehr gut aussehende Artikel von dir ist, den ich gesehen habe.Dann hast Du noch nicht viele von mir gesehen, oder kannst Qualität nicht erkennen. ;)
Ich habe die Fälschung in meinem Bild daran erkannt, dass die BUK seitenverkehrt ist und die Zeichen (3x2) dementsprechend spiegelverkehrt drauf sind (Ebenso erkennbar die Platten links in der Mitte da wo der Auspuff eigentlich sein sollte).Das sieht auf Deinem Bild nicht so aus, als sei die Nummer spiegelverkehrt:
Da ich selber nicht so in die Tiefe gehen kann kurz: ich weiß aber das wenn man eine richtig gute Fälschung machen will, die zusammengesetzten oder veränderten Komponenten auf einen hochauflösenden Druck, Beamer oder Plasma laufen lässt und von da nochmal abfotografiert/abfilmt. Mit der Methode lassen sich fast alle Signaturen oder von dir beschriebenen Diagnosemöglichkeiten neutralisieren.Also die Signaturen bekommt man auch mit Software raus, ist aber nicht das Ziel, fehlen sie, ist das auffällig, normal sind sie bei einer guten Fälschung dabei und wurden nur verändert. ;) Interessanter wäre da künstlich ein Sensorrauschen über das gesamte Bild gleichmäßig zu legen. Auch könnte anderes Rauschen zuvor raus gerechnet worden sein.