Effective Message Hiding with Order-Preserving Mechanisms
CoRR(2024)
摘要
Message hiding, a technique that conceals secret message bits within a cover
image, aims to achieve an optimal balance among message capacity, recovery
accuracy, and imperceptibility. While convolutional neural networks have
notably improved message capacity and imperceptibility, achieving high recovery
accuracy remains challenging. This challenge arises because convolutional
operations struggle to preserve the sequential order of message bits and
effectively address the discrepancy between these two modalities. To address
this, we propose StegaFormer, an innovative MLP-based framework designed to
preserve bit order and enable global fusion between modalities. Specifically,
StegaFormer incorporates three crucial components: Order-Preserving Message
Encoder (OPME), Decoder (OPMD) and Global Message-Image Fusion (GMIF). OPME and
OPMD aim to preserve the order of message bits by segmenting the entire
sequence into equal-length segments and incorporating sequential information
during encoding and decoding. Meanwhile, GMIF employs a cross-modality fusion
mechanism to effectively fuse the features from the two uncorrelated
modalities. Experimental results on the COCO and DIV2K datasets demonstrate
that StegaFormer surpasses existing state-of-the-art methods in terms of
recovery accuracy, message capacity, and imperceptibility. We will make our
code publicly available.
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