Digital media’s rapid evolution has created a number of problems for content creators, including the unlawful reproduction and dissemination of video content in particular. If an infraction occurs, forensic watermarking can be used to track down the exact source of the leakage by adding undetectable data into video recordings. It’s possible that watermarking techniques are vulnerable to geometric distortion attacks, which cause a misalignment of the inserted watermarks.
There must be watermark synchronisation to protect against geometric distortion attacks. This synchronisation of the watermark embedding and extraction locations is critical to the design of a viable watermarking system. “Patch” is a common term for this area. This can be done by taking advantage of the properties of the video, like texture, edges and motion to synchronise watermark sites. Using features as a reference point for geometric distortion assaults, referencing features can alleviate synchronisation difficulties. DRM protected content can thus be safeguarded against piracy using feature-based watermarking methods. Feature-based solutions.
Introductory step in the watermarking process is content analysis in order to extract relevant features. It is then possible to create patches by comparing the features. Patches of video are injected with watermarks throughout this process. During watermark detection, all patches are evaluated to find the watermark. If the watermark is correctly recognised by at least one patch, proof of ownership can be effectively demonstrated.
As a result, the distribution of feature points is a critical consideration for creating strong video watermarking solutions. As a result, the size of feature points’ surrounding areas should be taken into consideration. Because of the small size, the feature points are more likely to be concentrated in the textured portions of the model.. As opposed to being spread out, feature points get isolated if the size is increased excessively If you want the feature points to have a homogeneous distribution, you can use a circular neighbourhood constraint to pick only the strongest features.
It is common practise to place watermarks only in the most critical parts of a video. Mosaic frames from the source video can also be used to detect feature regions. By placing the watermark in these critical parts of the movie, the algorithm is better able to withstand degradation, which can distort the entire video as a result.
Image-based Here, the scene is represented just by its 2D projection, which are photos acquired by cameras. It is possible to watermark image sequences that record a 3D scene and extract the watermark from any rendered image generated for any arbitrary view angle, as opposed to the first two methods, which only protect the watermark information for the two key components of 3D scene representation (geometry and texture). If you’re using dynamic watermarking, you may embed information on the video asset while it’s being played back at the user’s end, such as the user’s email, date and time of watching, their IP address, or even their business logo. Because of their dynamic nature, they provide additional protection for confidential content that is not intended to be shared or altered. DAI (dynamic ad insertion) is also activated via dynamic watermarkin in order to optimise addressable ad income.
DRM video protection techniques such as watermarks are not sufficient on their own, but when used in conjunction with other measures, they can help to safeguard the intellectual property of the content owner and aid to trace the source of any alleged infringement. They also serve as a helpful reminder to users about their own and others’ rights to the content they’re using.