Generative Steganography via Live Comments on Streaming Video Frames
Yuling Liu, Cuilin Wang, Jie Wang, Bo Ou, Xin Liao · IEEE Transactions on Computational Social Systems · 2024
Generative text steganography has received considerable attention in the covert communication community for the benefit of sending secret messages without the need to modify carriers. Existing methods typically choose the next word when generating a stego-text based on conditional probability encoding of candidates, which may lead to generating inadequate words for the underlying secret message. How to generate a semantically controllable stego-text with a high capacity on secure embedding of a secret message is a main challenge. We address this challenge by proposing a new paradigm to generative text steganography that takes advantage of certain social media through apparently normal behaviors from the sender. In particular, we make use of the live commenting feature provided by public video sharing platforms (PVSPs), which allow viewers to make comments on video scenes that will fly on screens when the scenes are shown. We show that this feature can be used to construct a generative steganographic system. The sender generates at random a number of distracting words and a certain invertible matrix called W-dmatrix based on the total number of message words and distracting words. The sender then transforms a sequence of indexes of these words to a sequence, selects one or more videos with a sufficiently large number of total frames, and generates a comment on each frame in the sequence. The receiver extracts commented frame indexes, uses the shared stego-key to generate the same W-dmatrix as the sender, and obtains the secret message using the inverse of the matrix. The stego-key consists of a vocabulary generator and a W-dmatrix generator (WMG) based on pseudorandomly generated numbers. To generate comments on frames that conform to comments made by viewers, we devise a neural ResNet-LSTM model to generate a comment for an input image based on its content. Theoretical analysis shows that commented video frames (CVF) is covert, secure, efficient, and feasible to conceal any message of arbitrary length. We implement CVF and present evaluation results from multiple aspects that our work outperforms the existing stego-methods.