计算机科学
格子(音乐)
特征提取
人工智能
模式识别(心理学)
物理
声学
作者
Fan Yang,Hao Cheng,Shanxiang Lyu,Jinming Wen,Hao Chen
标识
DOI:10.1109/tifs.2024.3402948
摘要
This paper delves into the challenges of spread spectrum (SS) watermarking extraction, considering both blind and non-blind extraction scenarios, within the framework of lattice decoding. The orthogonality of carriers plays a crucial role in the accuracy of extraction, impacting the bit error rate (BER). When carriers lack sufficient orthogonality, conventional blind extraction methods such as multi-carrier iterative generalized least-squares (M-IGLS) and non-blind extraction techniques like MMSE-based schemes encounter performance degradation, posing difficulties in accurately recovering hidden data at the receiver end. To address these challenges, we propose two novel SS watermarking extraction approaches by integrating precise lattice decoding algorithms. Firstly, we introduce the highly accurate yet computationally efficient successive interference cancellation (SIC) algorithm to augment M-IGLS, resulting in a new method termed multi-carrier iterative successive interference cancellation (M-ISIC). Secondly, we adapt the near-optimal sphere decoding (SD) technique for non-blind extraction in SS watermarking. Theoretical analysis and experimental simulations showcase that our proposed M-ISIC and SD methods outperform M-IGLS and MMSE-based detectors, particularly in scenarios where carrier orthogonality is limited, achieving lower BER. Our code is available at https://github.com/shx-lyu/M_ISIC.
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