Combating Synthetic Media: How Large Platforms Can Detect AI-Generated Music And Content

Detecting synthetic audio is not the same problem as detecting synthetic text, and the difference is mostly economic. One engineer's work suggests the answer lies less in the recording than in everything around it.

A track arrives at a large digital platform through a distributor. It has a title, a credited artist, cover art and a genre tag. Nobody at the platform listens to it. Within hours it sits in the catalogue, available to anyone who searches for it, and eligible to earn.

For most of the streaming era that sequence was unremarkable, because the rate at which music could be made was limited by the people making it. Generative tools have removed that limit. A catalogue that once grew at the speed of human production can now absorb as much material as anyone cares to generate.

Where synthetic music differs from virtually all other forms of synthesized media is that while fabricated visual elements (text/images) create doubt with the viewer regarding what he/she is seeing; fabricated audio on a streaming service steals money from working musicians. This is due to the fact that streaming royalties are generally paid from a common pool and each time someone streams to an artificially created track, that is a stream that did not go elsewhere.

The issue falls more into the realm of payment fraud than misinformation and there is a deadline. Once a pay period ends, the money has moved. Bharath Kandati focuses on determining the decision-making process prior to that happening. He is a software engineer at a major streaming company working on large-scale data platforms and states that he has successfully implemented analytical pipeline integration and machine-learning workflows using various

signals including audio-metadata, behavioral-patterns and content-distribution-signals to detect potential fake or altered recordings.

This type of work looks at the surrounding signals of a recording versus examining the actual recording itself. There is a significant obstacle in analyzing audio for forensic purposes. Audio is compressed for streaming delivery and the compression required for efficient delivery removes high-resolution details within the frequency spectrum where a machine-generated signature resides. Standard heuristic approaches also fail. Unusual consistency in repetitive patterns can indicate fabrication in written materials. Repetition based upon a grid is exactly how musical structure works. Additionally, unlike written documents, audio cannot be "skimmed" and assessing an image quickly by viewing it in seconds, whereas assessing a song requires listening to it for the duration of the song.

As such, the evidence expands outward. According to Kandati, the systems he has developed collect multiple signals simultaneously from three areas: metadata associated with files; listener interaction with files; and behavior exhibited by accounts and paths through which files are transmitted. Fraud at scale is always conducted via transmission channels and channels leave markings even if the file does not. An individual anomalous track represents very little. Tracks uploaded in batches at the same time having the same template represent a different type of entity.

He refers to the remainder of his work as developing large-scale distributed data-processing architectures capable of evaluating vast amounts of event and content-signal information, as well as providing contributions to internal tools enabling engineers and analysts to investigate questionable trends without requiring specialized personnel. The environment in which these systems operate evaluate millions of content-interactions and signals daily. The number referenced above reflects the total operation of the platform, not an individual's output, and it is why he believes that reviewing tracks manually is not a viable method at the point-of-entry.

What types of results systems produce is somewhat less than what it appears to be. A model can state whether or not generative signatures exist within a recording. However, a model cannot determine what action a platform should take against producers utilizing machine-based mastering tools to master tracks or producers creating arrangements containing machine-generated elements, singing over them.

Automating aspects of music production has been occurring for many years. Determining the boundary between an automation tool and a substitute for creative input is a function of policy rather than measurement. What detection models provide is what exists. Another mechanism must be determined to define what actions should be taken as a result.

The asymmetric nature of error impacts the engineering process just as significantly as does the technology. Missing something diffusely loses some money from a pool and creates no clear victim that can express dissatisfaction. Flagging something incorrectly causes harm directly and specifically damages one identified artist's ability to earn a living and their professional reputation. Therefore, a system designed to treat missing something and flagging something incorrectly similarly will likely be mis-designed.

In terms of his current role, Kandati believes that none of these layers stand alone. Detection models decay rapidly as the capabilities of generators improve. Behavioral analysis identifies coordination but provides limited insight into individuals uploading content intentionally and carefully. Human judgment is accurate however it does not scale.

"The future of trusted-platform systems includes a combination of machine-learning based detection models, large-scale behavioral analyses and human review," Kandati states.

Additionally, he asserts that platforms will need to continue investing in infrastructure that can be reprioritized as new generation methodologies develop. Detection models created once and left operational degrade continuously since the opposing party continually evolves.

Currently, the burden rests at the conclusion of the transmission chain. Generation tools rarely label what they generate. Distributors rarely assert anything about how tracks were produced. Platforms therefore analyze the residual evidence left by tracks after they arrive at their destination, which is an inefficient method of acquiring knowledge that could have been acquired at the time a track was created. As such, attention throughout the industry began shifting toward preserving provenance that accompanies works as opposed to inferring provenance post-arrival through tracking behaviors subsequent to the creation of a track.

"Ensuring preservation of digital trust during the era of generative AI will necessitate both advanced technical systems and reasonable governance frameworks," Kandati claims.

The technical aspect of this statement currently remains under development in analytical pipelines and signal models within every large platform that transmits audio. The governance aspect of this statement – which determines what any given set of signals indicates relative to an individual receiving those signals --lags behind.

Published on: Tuesday, August 25, 2026, 08:46 PM IST