Digital rights management(DRM) is a form of copyright protection for digital media, such as ebooks, DVDs, video content, and other digital assets. Creators of intellectual property (IP) rely on DRM to prevent the unauthorized redistribution of digital media and to restrict how consumers can replicate purchased content. DRM is the result of the Digital Millennium Copyright Act (DMCA), a copyright law enacted to protect the intellectual property of copyright holders and creators from online piracy and peer-to-peer file exchange programs.
Digital rights management embeds a code in digital media that prevents it from being copied, stipulates how long content can be accessed for, or limits the number of devices that the media can be installed on. DRM technology makes it impossible to pirate digital content in the first place, giving creators a proactive approach to protecting their IP.
DRM software gives creators of intellectual property complete control over the use, editing, copying, or printing of a document or file. DRM software protects sensitive emails from being copied or forwarded, DVDs from piracy, and controls the copying or printing of ebooks. A digital rights management solution from Caplinked allows you to give users the freedom to download files for external use and revoke access to digital content even if it has been downloaded. Their DRM software solution is plugin-free and provides real-time activity analytics for insight optimization and streamlines your workflow so that you can improve efficiency.
Copyrights and DRM
Traditional copyright law protects intellectual property, such as written material, video content, and music. Digital copyright law is designed to protect digital content such as ebooks, games, website content, blogs, emails, and computer programs. Copyright law focuses on the legal remedies after a copyright holder’s rights are violated. DRM focuses on preventing violations in the first place given how impossible it is to identify and take action against every person who violates the copyright.
How does DRM work?
There are many approaches to DRM, but the most common forms use a code embedded into digital content. DRM can limit the number of times digital content is copied or printed as well as the number of devices that the content can be installed on. It limits the amount of time consumers can access content and on what mobile devices the content is compatible. DRM can limit access to digital content if devices are using certain plugins or programs, and it can cause a distortion to the content if it’s copied.
Limitations and Drawbacks of DRM
Even the most innovative DRM technologies can be bypassed by creating decryption keys, disabling DRM, deleting DRM encryption, or using a separate device or program to record content during playback. The DMCA has made DRM circumvention a criminal offense, but the enforcement of legal action is difficult. A drawback to DRM is consumer dissatisfaction. Under copyright law, the purchaser is able to make personal use copies according to fair use, but DRM may prevent the purchaser from making legal copies. Some DRM systems require the authentication of users through an online server, but if the server goes down, users can’t access digital content.
One way to improve DRM is with the application of machine learning. Within artificial intelligence is machine learning (ML) which gives computer systems the ability to learn from their experiences without being programmed to do so. Machine learning methods fall under the umbrella of deep learning, while machine learning algorithms are categorized as supervised or unsupervised learning. The supervised machine learning model allows a program to apply previously gained knowledge to new data using pattern recognition and labeled datasets. The machine is shown how to perform a function and then learns how to perform the function based on training data.
The unsupervised learning model uses unlabeled data to train an algorithm, allowing it to perform functions using unstructured datasets and more complex tasks than supervised learning. According to Future Insights, there are several practical applications of machine learning including classification of datasets, image recognition, extracting relevant information from video surveillance, natural language processing, financial security, computer speech recognition, the prediction of automobile traffic, predictive models for medical services, and social media analysis and optimization. An ML approach to DRM can better detect the unauthorized use of digital content and intelligently protect creators of intellectual property.
It’s hard to keep intellectual property protected in the digital age, but with the right approach to DRM, you can effectively safeguard your digital content from unauthorized use.