Media, entertainment companies turn to AI to spot copyright infringement

The use of AI for detecting copyright violations is steadily becoming a common practice in intellectual property enforcement. Machine learning models, drawing on vast datasets, significantly improve accuracy in identifying copyright breaches—helping distinguish between authorised and unauthorised uses with greater precision.

For instance, YouTube’s Content ID is an automated system that identifies and manages copyrighted content on the platform. In the latter half of 2023 alone, YouTube dealt with over one billion such cases, issuing strikes when copyrighted material was flagged and taking down videos accordingly. Multiple violations can even lead to removal of the specific channel.

Similarly, Meta’s Rights Manager is a video, audio, and image-matching tool that helps rights holders track and manage their content across Facebook and Instagram.

“The legal robustness of AI lies in its ability to support in spotting violations and in reducing liability exposure. The ambivalent situation is, AI is both a disruptor and a solution. It can play a critical role in monitoring, detecting, and removing unauthorised use of protected works, strengthening enforcement by analysing vast volumes of digital content with a speed and accuracy, impossible for human reviewers,” said Gaurav Sahay, founding partner, Arthashastra Legal.

AI systems can identify audio fingerprints, video frames, and metadata by comparing uploaded content against copyrighted material. Based on this, they can enforce rights through blocking, monetization, or tracking, Sahay added.

According to Namrata Pahwa, advocate at Delhi High Court, AI plays a major role in spotting and removing copyright violations in the music and entertainment industry through tools like audio fingerprinting, pattern recognition, and real-time monitoring.

“Platforms such as YouTube’s Content ID and Meta’s Rights Manager automatically flag, block, or monetise infringing content, while third-party services like Audible Magic and Pex help track unauthorised uses across platforms. In India, Amazon’s Brand Registry and Project Zero use AI to proactively block fake listings and empower brands to directly remove infringements, which is crucial given the scale of counterfeit merchandise,” said Pahwa.

She added that startups like Stylumia are also providing AI-driven trend and market insights, helping fashion and entertainment firms strengthen their IP strategies.

AI’s impact on music

AI has disrupted the music industry more than ever, with companies using algorithms trained on songs of established artists to create new music—fuelling concern and lawsuits.

Prof. Pratyush Banerjee, associate professor – communication, OB & HR (organized behaviour and human resource) at IMI Bhubaneswar said that one way to counter the rise of AI-generated music is by detecting such content early—with the help of AI tools. For example, Covernet, developed by Match Tune, uses deep learning-based audio fingerprinting algorithms to identify potential AI-created content.

Though adoption of legal and AI tools to detect AI content and protect artistic property is still at a nascent stage in India, experts noted that platforms like Apple Music, Spotify, and YouTube have already deployed such tools. Studios and music labels are building hybrid teams combining AI engineers with IP lawyers.

Hybrid solutions

Neither lawyers nor technologists can solve copyright challenges alone, industry experts point out.

AI specialists build scanning systems that sift through millions of songs, films, or live streams, spotting potential infringements at scale. Legal teams then step in to enforce rights, send notices, issue takedowns, negotiate licenses, or pursue litigation, said Nayantara Sanyal, partner, ALMT Legal.

Music labels, streaming platforms, studios and the likes are increasingly combining these skillsets to ensure that large-scale detection translates into effective legal action and protection of intellectual property and proprietary information.

That said, challenges remain.

“Adoption is growing but remains uneven across the industry. High upfront costs for model training, platform integration, and expert oversight pose a barrier to entry. A major impediment is access to high-quality training data. Accurate detection depends on large, diverse datasets to reliably match new content against copyrighted material, something many players struggle to obtain or license. While automation helps cut monitoring costs, human review is still needed to reduce false positives, keeping operational costs high,” Kalindhi Bhatia, partner, BTG Advaya said.