Detecting music deepfakes is easy but actually hard (2405.04181v2)

Abstract: In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. The ability to create credible minute-long music deepfakes in a few seconds on user-friendly platforms poses a real threat of fraud on streaming services and unfair competition to human artists. This paper demonstrates the possibility (and surprising ease) of training classifiers on datasets comprising real audio and fake reconstructions, achieving a convincing accuracy of 99.8%. To our knowledge, this marks the first publication of a music deepfake detector, a tool that will help in the regulation of music forgery. Nevertheless, informed by decades of literature on forgery detection in other fields, we stress that a good test score is not the end of the story. We step back from the straightforward ML framework and expose many facets that could be problematic with such a deployed detector: calibration, robustness to audio manipulation, generalisation to unseen models, interpretability and possibility for recourse. This second part acts as a position for future research steps in the field and a caveat to a flourishing market of fake content checkers.

• The paper demonstrates that simple convolutional neural networks can achieve over 90% accuracy in distinguishing real from fake music tracks in controlled tests.
• The study reveals that common audio manipulations like pitch shifting degrade detection accuracy, emphasizing the need for robust, adaptive models.
• The research highlights limited generalization across different deepfake encoders and underscores ethical and calibration challenges for practical deployment.

Exploring Music Deepfake Detection: A Comprehensive Study

Introduction to Music Deepfakes

Music deepfakes have joined the complex landscape of artificial media, where they pose unique challenges and ethical concerns, particularly within the music industry. This paper introduces a new field of music deepfake detection, leveraging advanced machine learning techniques to discern real musical tracks from sophisticated synthetic reproductions. The research showcases the successful application of convolutional models achieving exceptional accuracy rates, but it also dives into the complexities and limitations lurking beyond the impressive numbers.

The Detection Framework

Motivation: In recent years, user-friendly platforms have democratized the creation of deepfake music, leveraging sophisticated waveform-based generators. This widespread accessibility increases the risks related to copyright infringement, fraud, and unfair competition in the music industry.

Approach: The researchers selected specific waveform-based generators such as WaveNet, HiFiGAN, and others, focusing on their common characteristic features. They examined these models under the lens of known issues such as autoencoder artifacts and aimed to identify features indicative of synthetic origin.

Methodology: The paper utilized the FMA dataset, containing diverse music tracks across various genres to maintain a balanced scope. From these tracks, the researchers generated fake versions using multiple encoders at different settings. They then developed a convolutional neural network model to predict whether a given music sample was real or fake.

Initial Findings: Surprisingly, initial tests yielded accuracy levels exceeding 90% with simple model setups, highlighting an unexpected ease in distinguishing between real and synthetic tracks under controlled conditions.

Confronting Practical Challenges

Despite the high accuracy, the paper unraveled several areas of concern that complicate the deployment of such technology:

• Robustness and Manipulation: The paper found that common audio manipulations like pitch shifting or format reencoding significantly decreased detection accuracy. This suggests that real-world application would require ongoing updates and refinements, akin to antivirus software, to cope with evolving deepfake methods.
• Generalization across Encoders: The model’s ability to generalize to unknown encoders was limited. Training on one set of parameters offered little guarantee on performance against unseen configurations, representing a significant hurdle for practical implementation.
• Calibration and Interpretability: The research highlighted calibration issues wherein models might overestimate their confidence in predictions. This aspect is crucial for real-world applications where such claims can lead to false accusations and need stringent checks for reliability and fairness.
• Ethical and Deployment Concerns: The discussion also pointed towards the ethical dimension of deploying deepfake detectors, stressing the importance of transparent and regulated use to avoid misuse and reliance on mere technical solutions to complex socio-technical problems.

Future Directions and Conclusion

The paper does not just demonstrate feasibility but also acts as a cautionary tale about the complexities of reliably and ethically implementing deepfake detection technologies. Future work will need to focus on enhancing robustness to common manipulations, improving generalization across diverse and novel encoders, and developing more nuanced calibration and interpretability frameworks.

In essence, while detecting music deepfakes might seem alarmingly straightforward at first glance, the real challenge begins when considering practical, ethical, and robust deployment in a dynamically shifting landscape of digital content creation.