THInImg: Cross-modal Steganography for Presenting Talking Heads in Images
This work addresses the need for enhanced cross-modal steganography capacity for applications like secure transmission and digital rights management, representing a novel method for a known bottleneck.
The paper tackles the problem of hiding lengthy audio data in identity images to enable covert communication and copyright protection, achieving the ability to conceal up to 80 seconds of high-quality talking-head video in a 160x160 resolution image.
Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively. While previous approaches primarily concentrated on concealing a relatively small amount of information, we propose THInImg, which manages to hide lengthy audio data (and subsequently decode talking head video) inside an identity image by leveraging the properties of human face, which can be effectively utilized for covert communication, transmission and copyright protection. THInImg consists of two parts: the encoder and decoder. Inside the encoder-decoder pipeline, we introduce a novel architecture that substantially increase the capacity of hiding audio in images. Moreover, our framework can be extended to iteratively hide multiple audio clips into an identity image, offering multiple levels of control over permissions. We conduct extensive experiments to prove the effectiveness of our method, demonstrating that THInImg can present up to 80 seconds of high quality talking-head video (including audio) in an identity image with 160x160 resolution.