There are several forms of noise that can affect a digital camera. Some noise is very consitant through images, some noise becomes more visible depending on the conditions, some noise is entirely environmental, and some noise is a result of a camera's image processing.

  • The consitant noise is commonly known as 'dead pixels' or 'hot pixels' or 'stuck pixels'. Basically this is a manufacturing flaw that is common in high-resolution consumer cameras due to the practicalities and physical limitations involved in fitting 6-8 million subpixel-sensors inside of an area the size of your pinkey finger's nail. When you enlarge the image you might see a certain area that is always red or blue or green, no matter what the picture is of (though most visible when it appears in the middle of a sea of black). This noise isn't very useful for determining if an image was tampered with because it only affects relatively localized areas of the sensor (and editing can avoid those spots), but it can help you determine which camera it came from since relatively few cameras share the same 'hot pixels'. Somewhat recently camera manufacturers (at least in the DSLR market) have begun a process called 'mapping out' these pixels, where working pixels around a defective pixel donate their data to hide the flaw.
  • The noise that becomes more visible depending on the conditions of a camera is what most people think of when they hear 'noise'. As there is less light and the gain is increased on the image sensor this noise becomes more visible. Also as the temperature increases this noise tends to increase. As the integration-time (exposure time) is extended to the 1+ second range this noise can become more visible. Since this noise generally affects the entire sensor/image area it is more useful to see if an image was tampered with. But since it depends on environmental conditions it requires images taken in specific conditions to positively identify a camera. Also some of these noise characteristics are rumored to change over time and become more pervasive as the camera ages, but unless a camera's sensor is physically damaged (ie, 30-second exposures of the sun at high-noon on a clear bright day) these changes are mild.
  • Noise that is purely environmental is something like taking pictures near a microwave radio tower. Weird electro-magnetic interactions can occur and produce a very strong and consistant pattern through images. I've only seen a handful of examples with this effect, and it is very weird. It doesn't happen very often either. In the space/satellite environments there is other radiation that can affect images, but they take special measures such as baking-out a CCD to remove heavy-ion buildup on the sensor.
  • Some noise is caused by the camera's image processing. Generally you can find 'halos' surrounding edges in images, which is an effect from sharpening. JPEG compression is also something the camera does, and the effects here are visible (but difficult to predict). Finally there is data quantization that is performed to the sensor's image values during the conversion to JPEG's gamma curve. Generally a consumer camera will only produce a 24-bit JPEG image w/ 8-bits per channel. Professional cameras can produce a 14-bit RAW image w/ 14-bits per color that can be stored in a 48-bit TIFF image w/ 16-bits per channel. Some professional cameas (most notably Nikon's Compressed NEF/RAW format on the Nikon D70) perform a type of quantization to compress/drop data in the 'highlights' and produce gaps in the highlights (specalized software is needed to even notice these gaps, but some image-quality crazy guys found this after being unsatisfied with regular processing of their images). It is unlikely that this can be used to identify images on the internet since 8-bit JPEG images can't even represent such sublte changes and performs even more quantization in the first place... Overall you can only get an idea of the camera manufacturer from this kind of noise and won't usually be able to identify a specific camera, but every little bit apparently helps....and if a camera produces a 'RAW' image you can create a JPEG image that looks a lot like that camera's normal JPEG images, but you can't do a whole lot to an already processed JPEG without being easily identified as being edited.


If you have 300 images per camera and can identify most of these major kinds of noise among a large sample of images you could organize them by noise characteristics. Then when you have a camera to produces similar noise characteristics you can strongly say that the images were taken with that camera.

The problem is identifying noise...with professional DSLR cameras in good-light conditions though there is relatively no visible noise in images. But as there becomes less light noise becomes more visible and can be used to identify images. Many consumer point-and-shoot cameras have visible noise and lens abberations in nearly all of their images, and are relatively easy to identify. With a good understanding you should be able to identify the differences between a Digital P&S and a DSLR camera and get at least 10 right in this quiz (note, you need to understand the differences between lenses in these formats to do well in the quiz): DSLR or Digicam