Adversarial Aesthetics: Using Spectrogram Steganography to Interrogate AI Ethics in Electronic Music
Year: 2026
Type: conference-paper
Abstract: Audio steganography—the practice of concealing information within sound—has emerged as a creative tool in electronic music production, with artists embedding visual data in the time-frequency domain that is audible as texture but reveals itself clearly only when rendered as a spectrogram. Artists are increasingly adopting steganographic techniques, known as data poisoning, to prevent their works being used to train AI without their consent. By embedding imperceptible adversarial noise directly into their work, artists can corrupt the feature-extraction process of unauthorised AI model development, while the data remains perceptually unchanged to humans. This work, through the medium of two audio-visual artworks incorporating steganographic processes, places a critical lens on the socio-cultural impacts of technological development and its historically undulating relationship with electronic music. Through this, we hope to promote discussions on responsible technology development that recognises the value of human creativity, an imperative dialogue as data-driven AI continues its rapid expansion.
Authors: Alexander James Williams, Yutian Hu, Stefan Lattner, Mathieu Barthet
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7168188024