With the deepening trend of paperless workflows, signatures as a means of identity authentication are gradually shifting from traditional ink-on-paper to electronic formats. Despite the availability of dynamic pressure-sensitive and PKI-based digital signatures, static scanned signatures remain prevalent in practice due to their convenience. However, these static images, having almost lost their authentication attributes, cannot be reliably verified and are vulnerable to malicious copying and reuse. To address these issues, we propose AuthSig, a novel static electronic signature framework based on generative models and watermark, which binds authentication information to the signature image. Leveraging the human visual system’s insensitivity to subtle style variations, AuthSig finely modulates style embeddings during generation to implicitly encode watermark bits-enforcing a One Signature, One Use policy. To overcome the scarcity of handwritten signature data and the limitations of traditional augmentation methods, we introduce a keypoint-driven data augmentation strategy that effectively enhances style diversity to support robust watermark embedding. Experimental results show that AuthSig achieves over 98% extraction accuracy under both digital-domain distortions and signature-specific degradations, and remains effective even in print-scan scenarios.

Storage-compute disaggregation has become a mainstream paradigm in cloud computing, yet data lake workloads introduce distinct prefetching challenges: massive numbers of small files, interleaved multi-tenant streams, and frequent one-time accesses. Under these conditions, traditional sequential, correlation-based, and semantic prefetching methods become ineffective, leading to cache inefficiency and high latency. We propose SemanticPrefetcher, a lightweight, semantic-aware prefetching mechanism that incrementally constructs meaningful access streams at runtime. The system operates in three stages: it tokenizes file paths or object names into a unified semantic representation, clusters related requests into coherent streams despite multi-tenant interleaving, and detects naming regularities to predict future accesses. This design transforms mixed and seemingly disordered requests into predictable access flows without application modifications. Implemented on JuiceFS, SemanticPrefetcher reduces end-to-end execution time by up to 39.6% and read latency by 79.3% compared to state-of-the-art baselines. These results demonstrate that implicit semantics in file paths can be effectively leveraged for robust and efficient prefetching in cloud-scale data lakes.

Digital steganography is the practice of embedding secret information in original normal data to enable covert communication. With the rapid advancement of generative models, generative steganography has gained renewed vitality. As a key medium on the Internet, audio has also become a focus of steganographic research. However, existing audio steganography methods rely on traditional audio synthesis models, which often suffer from suboptimal synthesis quality. In contrast, diffusion models perform well in audio synthesis tasks, but there is a lack of targeted secure audio steganography methods based on them. In addition, existing steganography schemes are generally limited to transmitting only the steganographic object, and other key elements need to be negotiated in advance, which limits their practicality. To address these issues, we propose CoAS, a composite audio steganography method based on text and speech synthesis. Firstly, we use a provably secure linguistic steganography method to embed the synchronous side information required for audio steganography, and then replace the gaussian noise in the diffusion models with message-driven sampling during the audio generation process. Both theoretical analysis and experimental results validate the security and practicality of our composite steganography method in the real world. Audio samples are available at https://meterial.github.io/coas.github.io.

In recent years, with the popularity of social media applications, massive digital images are available online, which brings great convenience to image recreation. However, the use of unauthorized image materials in multi-source composite images is still inadequately regulated, which may cause significant loss and discouragement to the copyright owners of the source image materials. Ideally, deep watermarking techniques could provide a solution for protecting these copyrights based on their encoder-noise-decoder training strategy. Yet existing image watermarking schemes, which are mostly designed for single images, cannot well address the copyright protection requirements in this scenario, since the multi-source image composing process commonly includes distortions that are not well investigated in previous methods, e.g., the extreme downsizing. To meet such demands, we propose MuST, a multi-source tracing robust watermarking scheme, whose architecture includes a multi-source image detector and minimum external rectangle operation for multiple watermark resynchronization and extraction. Furthermore, we constructed an image material dataset covering common image categories and designed the simulation model of the multi-source image composing process as the noise layer. Experiments demonstrate the excellent performance of MuST in tracing sources of image materials from the composite images compared with SOTA watermarking methods, which could maintain the extraction accuracy above 98% to trace the sources of at least 3 different image materials while keeping the average PSNR of watermarked image materials higher than 42.51 dB. We released our code on https://github.com/MrCrims/MuST

In this paper, the optical wave propagation in lossy waveguides is described by the Helmholtz equation with the complex refractive-index, and the Chebyshev pseudospectral method is used to discretize the transverse operator of the equation. Meanwhile, an operator marching method, a one-way re-formulation based on the Dirichlet-to-Neumann (DtN) map, is improved to solve the equation. Numerical examples show that our treatment is more efficient.