BirdsSong: A stylized generative audio steganography

Abstract

Recent advancements in stylish content generation technologies have revolutionized image synthesis, enabling the creation of high-resolution images with precise control. Motivated by this progress, we present BirdsSong, a novel audio steganography framework. BirdsSong aims to improve steganalysis resistance, increase steganographic capacity, enhance audio quality, and diversify styles compared to traditional methods. The framework includes an audio style extractor to separate content and style, a generator to combine style and content vectors without damaging audio quality, and a message extractor to recover the secret message accurately. Experimental results demonstrate that BirdsSong outperforms baseline methods in steganographic capacity, audio quality, style diversity, and resistance to steganalysis tools.