Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.
May 16, 2025
As an important technique to achieve covert communication, steganography has developed greatly in pursuit of the secrecy of concealment suggested by Shannon. The most widely used scheme is called adaptive steganography which is composed of two phases: the distortion calculation phase and the adaptive steganographic coding phase. Conventionally, adaptive steganography assumes a noise-free lossless channel between the sender and the receiver. However, in real-world applications, the stego (cover media with secret messages embedded) would suffer from various lossy operations and be modified during transmission, leading to extraction error at the receiver. In this paper, robust adaptive steganographic coding is considered. We formalize the problem to be a problem of finding the maximum embedding rate of a special communication system with a normalized adaptive distortion function and a non-stationary memoryless sequence of discrete channels such that the secret message can be communicated without error. The theoretical rate-distortion bound of the problem is established which is a great leap forward in the research field of robust steganography. By modeling the noisy channels in real-world applications into non-stationary discrete memoryless channels (DMCs), our bound can be used to evaluate the existing robust methods and will serve as the ultimate goal for the design of new practical robust adaptive steganographic coding algorithms.
Apr 8, 2025
Cloud infrastructures have become increasingly popular among distributed applications in the modern era due to their efficient failure recovery, global reach, mobility, and service integration. Highest Random Weight (HRW) is one of the Consistent Hashing (CH) schemes that offers indefinite scalability while maintaining consistency, lookup, minimal dispersal, and load balancing for cloud environments. However, current versions of HRW and other CH algorithms offer high memory costs, exchange excessive messages for TCP connections, and sacrifice performance when considering large scalable distributed systems due to extensive rehashing or O(w) comparisons among $w$ working nodes. This paper suggests a weight function for the Highest Random Weight (HRW) scheme, termed as Multiplication Modulo-based Highest Random Weight (MM-HRW) scheme. In the MM-HRW scheme, the entire key range can be divided into several nonoverlapping ranges, and each range corresponds to a working node. By applying binary search on the ranges, we can find out the working node of a key with O(log (w)) comparisons. Additionally, we present the corresponding algorithms, implementations, and mathematical validation to support the experimental findings. The results demonstrate that MM-HRW consistently achieves an optimal memory footprint, validating its scalability across various scenarios. Additionally, by considering large cluster size, MM-HRW scheme achieves the highest lookup rate while maintaining O(log (w)) comparisons across all scenarios.
Mar 22, 2025
We consider using polar codes for lossy source coding of a symmetric Discrete Memoryless Source (DMS) with a time-varying distortion measure instead of the conventional fixed single-letter distortion measure in this paper. First, the specific lossy source coding problem is formally stated and the rate-distortion bound is developed. Second, with the theoretical pursuit, a polar codes-based scheme is proposed. The code construction step of the scheme takes in the non-stationary sequence of test channels corresponding to the test channel model between the source sequence and the recovered sequence of the lossy source coding problem. Then, a Successive Cancellation (SC) encoder is employed to perform the actual encoding of the source sequence. We develop the optimal characterizations of the test channels, with which the proposed method shows near-optimal performance very close to the rate-distortion bound. We further prove that given the sufficient condition of fast source polarization, the proposed method under a randomized SC encoder achieves the rate-distortion bound for the given source settings when the source length N tends to infinity.
Jan 14, 2025