Watermark detection best practises for premium video material on OTT platforms should be adhered to
While content owners rely on concurrency management techniques and DRM protected content to block unauthorised access, these solutions are ineffective in deterring piracy on the user end. Digital assets are increasingly being protected against infringement and piracy through the use of watermarking systems.
There are several different ways to watermark video footage, but the most common is to insert a signal or codec into the video itself. In the event of an infringement, watermarks can also be utilised to identify the source of the leakage of the content. Therefore, watermark detection is the most important step in identifying and deterring piracy attacks in DRM protected content The end-to-end discovery, detection, analysis, enforcement, and reporting services that the video watermarking system integrates into make it easier to find the source of leakage and take action as soon as possible.
Web crawlers must be used to detect pirated content at all times by content producers. There must be a thorough examination of a wide range of resources in order to detect watermarks with any degree of accuracy. The detection service should also be able to identify which sites receive the most stolen content and frequently evaluate the effectiveness of the detection technique. Keeping up with changes in content distribution and piracy, both locally and globally, should also be a consideration. This guarantees that anti-piracy solutions get the most out of their resources and budgets, resulting in a higher return on investment.
Automated and artificial intelligence (AI) can be used to help content producers detect video watermarking, as well as other new technologies. Since the rise of OTT and VoD material, the number of websites and link aggregators that offer premium content for free has also increased tremendously in recent years. The amount of data that needs to be gathered and processed is enormous, which would be extremely time-consuming and demanding without the usage of automation.
As a result, the optimum strategy is a hybrid model that employs both automation and human verification. When a watermark payload is identified, the session database is used to locate relevant session information that matches the payload key value. A copyright enforcement strategy or a video distribution’s anti-piracy procedures can then be improved using this information.
Additionally, watermark preprocessing, also known as watermark generation, enables a greater level of security for watermarking. Compressed watermarking is preferred over spatial watermarking because decompression and compression of video data may not always be possible due to high storage capacity requirements for compressed watermarking. An MPEG stream that has been reencoded significantly increases processing time, making it unsuitable for real-time applications where embedding processes take place in parallel with compression. To make it more difficult for an attacker to reverse engineer, this data must always be generated on the server side using a randomised ID. Using a “hybrid” approach, the content is first preprocessed by the server to create various versions, and the watermark is then added or managed on the edge servers or at the client-side.
