No. This site is like a microscope -- it will show you data, but it does not draw any conclusions. FotoForensics includes tutorials to help you understand what to look for in the analysis results.In some cases, the analysis may not provide the answer you wanted. For example, you may want to know if a picture was edited. However, if the picture is a low quality, then the results may not permit identification of anything beyond "low quality, multiple resaves." As a concrete example, consider analyzing a picture from Facebook. Facebook strips out all original metadata and replaces it with their own metadata. So the metadata analysis will not identify anything beyond "Facebook". Facebook also resaves the image at a low quality, so the JPEG quality (JPEG %) will report a low quality image. Error Level Analysis will typically return a dark result with large colored rectangles -- indicating a low quality image and multiple resaves (a solid description of what Facebook provides). Even if the picture is visually altered, the algorithmic results may not detect much more than an image resaved by Facebook. The tutorials on this site identify some common applications and online services that leave tell-tale artifacts that are usually identifiable.
Error Level Analysis (ELA) is an algorithm that evaluates the error level potential of a JPEG image. JPEG is a lossy image format; every resave degrades the picture. The amount of degradation varies based on the number of saves. The first save loses a lot, the second save loses a little more, and by the 20th save, it is probably as low quality as it will ever get.
When a picture is modified, the changed parts have a higher error level potential than the rest of the image. ELA works by saving the picture at a known quality level (like a JPEG at 95%), and then determines how much changed. Edits and splices appear as regions with more change. See the tutorial for more detail.
The Error Level Analysis algorithm was publicly disclosed by Dr. Neal Krawetz in a white paper and presentation at the Black Hat Briefings security conference. The revised white paper and slides are from the 2008 conference in Washington, DC.
Krawetz, N., "A Picture's Worth: Digital Image Analysis and Forensics." Black Hat Briefings DC. 2008. <http://blackhat.com/presentations/bh-dc-08/Krawetz/Whitepaper/bh-dc-08-krawetz-WP.pdf>
Chicago Manual of Style citation
Krawetz, N., "A Picture's Worth: Digital Image Analysis and Forensics." Black Hat Briefings DC. 2008. Available from http://blackhat.com/presentations/bh-dc-08/Krawetz/Whitepaper/bh-dc-08-krawetz-WP.pdf
ELA measures the amount of change during a JPEG resave. When a digital photo is edited, the modified portions will have a different error level potential compared to the rest of the picture. Splices, drawing, and significant edits are usually visible as a significantly different error level potential.
There is a difference between real and authentic. A real photo of a forged document or a staged situation will not appear unusual under ELA. This is because the picture is real, even if the subject of the photo is not authentic. ELA does not identify the authenticity or other attributes related to the picture's subject.
ELA also does not detect all forms of digital manipulation; it only identifies differences in the JPEG compression rate. Digital modifications that do not significantly alter the error level potential, such as a minor color adjustment over the entire picture, may not be detected by ELA.
A very low quality picture that has undergone multiple resaves will have no more error level potential. A black result is informative: this picture (1) is not a camera original, (2) is very low quality, and (3) has been repeatedly resaved.
When the Error Level Analysis algorithm was disclosed in 2007, we intentionally did not release source code. As a result, every implementation is a variant of the algorithm. They all implement the same basic approach and all can be used to reach the same conclusions. However, different settings can lead to differences in the appearance of the ELA image.
For a proper experiment, the results must be repeatable. This system uses libjpeg-6b with a resave quality of 75% and a post-process brightness factor of 20. Different JPEG libraries and different parameters will generate different ELA images.
The results from an analysis are directly dependent on the image quality. You may want to know if something was added, but if the picture is a copy of a copy of a copy, then it may only detect the resaves. Try to find the best quality version of the picture.
For example, many pictures are hosted at Flickr. Flickr provides small, medium, large, and original images. The small, medium, and large are derivative images (resaves) created by Flickr. The "original" is whatever the user sent to Flickr, so the original will be the best quality. Similarly, pictures on news sites are usually resaved. If they have a tagline like "Source: AP Images", then go to the source and use that picture instead. News sites typically recolor, resize, and crop images before saving them at a very low quality. Go for the original source (or get as close as you can to the original source) to improve the image's quality and the results.
If you do not know where to start, then try TinEye. Many pictures on the web are resaved as they pass from user to user. TinEye does not know every picture, but it knows many pictures. If the picture is being passed around, then TinEye can help find the source (or at least a better copy of the image). In general, the biggest image is usually the best quality. (But some sites do scale images larger...)
First remember: any single analysis algorithm can generate noise that may result in a false-positive interpretation. You should confirm your results with other analysis methods. ("Observation" is always a good one, so is "common sense".)
Second, identify how the picture was modified. For example, scaling a picture smaller for the web will remove high frequencies and modifies every pixel. Are you seeing artifacts related to how the picture was processed, or are you seeing intentional deception?
Finally, who you tell is up to you. If it is from the mass media, then find out where they got it -- usually they purchase pictures from Getty Images, Reuters, AFP, AP Images, or other professional organizations. Contact the right people. Be polite, and tell them what you found. (Do not demand that they fire the photographer; if you are right, that will happen automatically.)
One warning: there is a difference between accusing someone of photo manipulation and libel/slander. (Consider using alternate wording like "it is my belief that" and "based on the following tests it appears that".)