READ THE PREVIOUS ARTICLES:.:: Digital Watermarking - Part I .:: Digital Watermarking - Part II DIGITAL WATERMARKS MUST BE UNOBTRUSIVE, INVISIBLE AND NOISELESS Discussing the practical use of watermarking, there are two main questions imposed:
1. Can a digital watermark do its job even if the original file is edited (e.g. if we change the file extension, signal/noise ratio, dimensions, etc.); 2. Can a digital watermark be physically removed and what happens if it is removed, i.e. is there a second-level protection of the original fileWe are familiar with watermarking algorithms that only work properly in immaculate surroundings not disturbed by any modification to the original file. Even the smallest hindrance causes them to lose their functionalities. The practical use of these algorithms is therefore very limited. On the other hand, there are algorithms that create stamps in such a way that no modification to the file can lead to their loss. You will, however, abandon this approach as soon as you see the changes made to the original file. Namely, it makes sense to conclude that such a watermark is too easy to notice or too well to hear (usually, as a noise or buzz). Being too strong, it disturbs the original signal. This solution is in marked contrast with the fact that the file should be digitally watermarked in such a way that the watermark is unobtrusive, invisible and noiseless. So we had to develop different algorithms for digital fingerprinting and different logic for each type of file (images, GIF, audio, video, PDF). Watermarks are difficult to detect, however, if one manages to identify them, he can also physically remove them from the file, or simply cover the signal with a high intensity of noise and make the watermark unusable. The question is, if the stamp is obscured or gone, how do you prove that it has ever been there, and how do you compare images, audio or video files from the web with those from the database?
RECOMMENDED SERVICES: >> Digital Image Fingerprinting >> Digital Audio Fingerprinting >> Digital Video Fingerprinting >> Digital Animated GIF Fingerprinting >> Digital PDF FingerprintingSECOND-LEVEL PROTECTION OF THE ORIGINAL DIGITAL FILE This is exactly what the purpose of the aforementioned second-level protection of the original digital file is. An author of the work being stamped will receive a so-called private fingerprint key, not publicly available and delivered via email exclusively. The private fingerprint key can be in the form of an audio file (30 second mono audio) or a greyscale image and contains the spectral characteristics of the original digital file. If you come across an image, audio or video on the web that you believe to be your copyrighted work which you have previously stamped (and which was obviously published without your consent) and you are unable to reconstruct that stamp from the said file, you should to do the following. Compare your original file with the image, audio or video in question by the method of calculating the degree of similarity (it is clear that the similarity will be measured big) and send us an email with a link to the file you want to examine, as well as the name of your original file. We will mathematically reconstruct the spectral characteristics of your original file by using an inverse software method and attach it to the file you want to examine.
DIGITAL FILE WATERMARKING AND COPYRIGHT PROTECTION TOOLS:
// Get an unobtrusive, invisible and noiseless digital watermark embedded in your file as its globally unique identifier and copyright protection tool //
.:: Add watermark to image .:: Add watermark to audio .:: Add watermark to video .:: Add watermark to animated GIF .:: Add watermark to PDF .:: Add watermark to YouTube video
// Submit a video and convert it to grayscale or black and white video, create an animated GIF image online, trim a video file, extract audio from video, extract video only, extract images, add watermark to video, or create a thumbnail //
FIND THE SIMILARITY BETWEEN IMAGES, AUDIO AND VIDEO FILES
// Compare two files online by measuring the similarity and computing the normalized cross-correlation //