Syndrome Trellis Code (STC) stands out as one of the nearly optimal steganographic coding methods to date. Its superior efficiency and performance have garnered significant attention. However, STC has a limitation: it struggles to handle unevenly distributed host signals and messages, preventing it from achieving the theoretical minimum distortion. To address this flaw, we introduce an enhanced STC scheme called Content-Aware STC (CA-STC). In our work, we propose modifying the codebook based on the statistical relationship between host signals and messages. This adjustment aims to reduce overall distortion. Additionally, we introduce a new parameter to strike a balance between complexity and embedding efficiency in the proposed method. Simulation results, including scenarios involving random data, demonstrate that our approach outperforms STC in terms of global distortion, effectively adapting to diverse scenarios. Code available: https://github.com/shx-lyu/CA-STC/.

The rising complexity and cost of training DNN models underscore the need for intellectual property protection. In this regard, DNN watermarking has emerged as a crucial safeguarding technique. This paper introduces FedReverse, a multiparty reversible watermarking method that ensures robust copyright protection and minimal performance impact. FedReverse is reversible, allowing collaborative watermark embedding post-training and enables complete removal with unanimous consent. It is resistant to Known Original Attacks (KOA), making watermark forgery and key inference difficult. Comprehensive simulations with MLP and CNN models on varying parameters show FedReverse’s robustness, reversibility, and minimal accuracy impact.

Image watermarking techniques have continuously evolved to address new challenges and incorporate advanced features. The advent of data-driven approaches has enabled the processing and analysis of large volumes of data, extracting valuable insights and patterns. In this paper, we propose two content-aware quantization index modulation (QIM) algorithms: Content-Aware QIM (CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithms aim to improve the embedding distortion of QIM-based watermarking schemes by considering the statistics of the cover signal vectors and messages. CA-QIM introduces a canonical labeling approach, where the closest coset to each cover vector is determined during the embedding process. An adjacency matrix is constructed to capture the relationships between the cover vectors and messages. CAMD-QIM extends the concept of minimum distortion (MD) principle to content-aware QIM. Instead of quantizing the carriers to lattice points, CAMD-QIM quantizes them to close points in the correct decoding region. Canonical labeling is also employed in CAMD-QIM to enhance its performance. Both schemes can be categorized as (key-aided) semi-blind watermarking. Simulation results demonstrate the effectiveness of CA-QIM and CAMD-QIM in reducing embedding distortion compared to traditional QIM. The combination of canonical labeling and the minimum distortion principle proves to be powerful, minimizing the need for changes to most cover vectors/carriers. These content-aware QIM algorithms provide improved performance and robustness for watermarking applications.

Conventional Quantization Index Modulation (QIM) has been tailored for diverse data hiding applications, yet it lacks the ability to achieve minimal distortion in physical layer steganography. To address this, we introduce an enhanced QIM scheme, Content-Aware QIM (CA-QIM). By adapting the codebook to the probability distribution of embedded messages and host signals, CA-QIM excels at handling non-uniformly distributed messages, resulting in significantly reduced distortion. Our approach outperforms other QIM variants in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and percentage residual difference (PRD), as demonstrated through simulation results. CA-QIM offers a promising solution for achieving improved perceptual transparency and robustness in steganography applications.