Both my cellphone and my iSight camera distort video (and audio) in very specific ways when they are operated near a demonic entity or during high periods of demonic activity, which include frame-jumping during playback, oversaturated demons, blurry or sharpened outlines of demons. They also both record chroma (or color noise), which, unlike the energy or radiation emitted by demons, is visible to the naked eye, but still could be used as an indicator of demonic activity in that its appearance is unique to such. Audio files play back white, pink or brown noise that sounds different from that of nearby machinery, wind, etc. Sometimes, though, the difference is minute to the human ear, depending on what else might be generating such noise at the time the audio recording was made.
Most people have not used high-end video editing software, but Final Cut Pro, for example, measures values of many types of properties of video and audio files on a granular level (i.e., to a degree that are imperceptible to humans). These properties are typically only useful to broadcasters, who must measure obscure properties of such files in order to ascertain playback quality on the variety of television or audio equipment the files may be broadcast to.
Final Cut Pro X videos scopes documentation
In an application such as the one I propose, however, these measuring tools could be used to find a "difference" between the values of properties of audio and video files that were made around demonic activity and an average made from a wide sample of a/v files that were not.
This, by the way, is how most fingerprint readers work that don't store an image of your fingerprint in a database (this is particular important for security; however, in law enforcement, images are required). When you register as a user of fingerprint recognition software that does not store images of your fingerprints, such as DigitalPersona's entire fingerprint recognition hardware and software product line, you are usually asked to register the same fingerprints multiple times. An algorithm is applied to all of the images of a given fingerprint in order to create a unique and reproducible hash. The hash value is stored in a fingerprint reader database, and is later compared to the hash value returned by the same algorithm the next time you touch the fingerprint sensor in order to use it (after having registered with it, of course). Here, a similar-in-concept algorithm could be applied to samples of values generated by various properties of demonic and non-demonic A/V files to make reproducible and reliable comparisons mathematically between the two kinds of files.
This is also how some spam filters work. A specially developed algorithm is applied to a set of e-mails identified by the recipients as spam. The algorithm uses the e-mail metadata and content of spam messages to identify a common mathematical pattern among them. Since most e-mail is easily discernible between spam and work-related messages, for example, the pattern generated by this algorithm should be vastly different between the two, as well. The only difference between algorithms for detecting fingerprints and spam messages is that spam-messaging trends evolve over time, particularly, when technology-savvy marketers learn how to skirt detection by these algorithms. So, algorithms used for spam detection must be continuously applied to all incoming messages defined as spam by recipients, and the pattern must also be continuously updated on all end-user workstations, so that new spam can be recognized as such before reaching an end-user's mailbox. Collecting and reporting new spam is arduous, in that a company making spam-filtering software must either hire employees to search for spam and add it to a database accessible by the detection algorithm, or end-users who use their spam-filtering product must have an easy (or, preferably, transparent) way of reporting spam.
NOTE | Some companies ship their software as an e-mail software plug-in, which watches a customer's spam folder for messages added by them. When the plug-in detects that a new spam message has been identified by the end-user, the message is sent by the plug-in to the software company for use by the detection algorithm. The new pattern is then silently sent to all other customers of the spam-filtering plug-in, so that only the reporting user had to take the time to mark a message as spam, while all other users (a number in the millions, like Yahoo! Mail) will not have to if they receive the same—or similar—spam message.
If a median set of values could be determined from A/V files made of demonic activity and from files that weren't—and the difference was theoretically possible to detect with some degree of reliability, you could use your cellphone to make a real-life demon detector.
I think there are some people who know what I'm saying here. Where are all the nerds that always used to gravitate around me? I bet they could figure this one out, as it wouldn't be too hard, I don't think. After all, two of the leading biometric security and spam-filtering software companies use algorithms developed by a single college student each.
