  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.

Computer Vision and Pattern Recognition