Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication.Provable security, which is a long-standing goal and key motivation, has been extended to languagemodel-based steganography.Previous provably secure approaches have achieved perfect imperceptibility, measured by zero Kullback-Leibler (KL) divergence, but at the expense of embedding capacity.In this paper, we attempt to directly use a classic entropy coding method (range coding) to achieve secure steganography, and then propose an efficient and provably secure linguistic steganographic method with a rotation mechanism.Experiments across various language models show that our method achieves around 100% entropy utilization (embedding efficiency) for embedding capacity, outperforming the existing baseline methods.Moreover, it achieves high embedding speeds (up to 1554.66 bits/s on GPT-2).The code is available at github.com/ryehr/RRC_steganography.

Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications.While previous work mitigates this fragility by limiting the context window, it significantly compromises text quality.In this paper, we propose the anchored sliding window (ASW) framework to improve imperceptibility and robustness.In addition to the latest tokens, the prompt and a bridge context are anchored within the context window, encouraging the model to compensate for the excluded tokens.We formulate the optimization of the bridge context as a variant of prompt distillation, which we further extend using self-distillation strategies.Experiments show that our ASW significantly and consistently outperforms the baseline method in text quality, imperceptibility, and robustness across diverse settings.The code is available at github.com/ryehr/ASW_steganography.

Predicting the dynamics of infectious diseases plays an important role in their prevention and control. However, traditional epidemic models, such as susceptible-infected-recovered (SIR), susceptible-exposed-infected-recovered (SEIR), and their variants, are often inadequate for accurately capturing behavior-driven and heterogeneous transmission at scale. In this study, we propose a nonlinear dynamic model-the susceptible-low-susceptible-exposed-infected-recovered-death (SLEIRD) model-that further extends the SEIR framework. SLEIRD explicitly differentiates susceptible populations by introducing a low-susceptible group adopting protective measures, models four infection channels from susceptible/low-susceptible to exposed/infectious groups, thereby providing a behavior-aware and structurally richer compartmentalization than classical models. To validate its effectiveness, we compare SLEIRD with other classical epidemic models using real-world epidemic data, predicting confirmed cases and deaths over five, ten, fifteen, twenty, and thirty days. Experimental results show that SLEIRD outperforms SIR, improving the root mean squared error (RMSE) for daily confirmed cases and deaths by 6.0%-46.3% and 31.1%-62.3%, respectively; compared with SEIR, SLEIRD reduces the average RMSE for daily confirmed cases and deaths by 6.8%-56.6% and 59.8%-79.6%, respectively. These findings indicate that SLEIRD more accurately simulates complex spread dynamics and offers a scalable, behavior-aware tool for data-driven epidemic forecasting with practical value for disease control and prevention.

Large language models have significantly enhanced the capacities and efficiency of text generation.On the one hand, they have improved the quality of text-based steganography.On the other hand, they have also underscored the importance of watermarking as a safeguard against malicious misuse.In this study, we focus on tokenization inconsistency (TI) between the sender and the receiver in steganography and watermarking, where TI can undermine robustness.Our investigation reveals that the problematic tokens responsible for TI exhibit two key characteristics: infrequency and temporariness.Based on these findings, we propose two tailored solutions for TI elimination: a stepwise verification method for steganography and a post-hoc rollback method for watermarking.Experiments show that (1) compared to traditional disambiguation methods in steganography, directly addressing TI leads to improvements in fluency, imperceptibility, and antisteganalysis capacity; (2) for watermarking, addressing TI enhances detectability and robustness against attacks.The code is available at https://github.com/ryehr/Consistency.

Since tokenization serves a fundamental preprocessing step in numerous language models, tokens naturally constitute the basic embedding units for generative linguistic steganography. However, tokenization-based methods face challenges including limited embedding capacity and possible segmentation ambiguity. Despite existing character-level (one tokenization-free type) linguistic steganographic approaches, they face the problem of generating unknown or out-of-vocabulary words, potentially compromising steganographic imperceptibility. In this paper, we focus on both embedding capacity and imperceptibility of tokenization-free linguistic steganography. First, we suggest that unknown words mainly result from low-entropy distributions and rigid coding rules used in candidate pools, thus we propose an entropy-based selection approach to flexibly construct candidate pools. Further, we present a lexical emphasis approach, prioritizing characters within candidate pools capable of forming in-vocabulary words. Experiments show that, across a range of high embedding rates, our approaches achieve considerably higher imperceptibility and text fluency, increase anti-steganalysis capacity averagely by 14.4%, and particularly reduce out-of-vocabulary rate averagely by 88.7%, compared to the existing state-of-the-art character-level steganographic methods.

Generative linguistic steganography aims to embed information into natural language texts to achieve covert transmission. However, currently in most approaches based on subword-supporting language models, the extraction process relies on tokenizing steganographic texts into tokens, which could cause segmentation ambiguity, leading to false results or failures of extraction finally. Despite several existing countermeasures (or disambiguation) that have been proposed, they are based on removing tokens of candidate pools, which render them incompatible from the sights of keeping imperceptibility, potentially incurring safety risks. To avoid it, we focus on tackling segmentation ambiguity with near-integrity of candidate pools. In this paper, we propose a near-imperceptible disambiguating approach via verification for generative linguistic steganography. First, this paper draws an all-case extraction method to obtain possible true extracted results. Further, length verification and checksum verification are presented to filter wrong extracted results caused by segmentation ambiguity. Experiments show that our disam-biguating approach outperforms the existing disambiguating approaches, on various criteria, including about 23.49 % higher embedding capacity, about 23.46 % higher imperceptibility and about 5.73% anti-steganalysis capacity of steganographic texts.

Segmentation ambiguity in generative linguistic steganography could induce decoding errors. One existing dis-ambiguating way is removing the tokens whose mapping words are the prefixes of others in each candidate pool. However, it neglects probability distribution of candidates and degrades imperceptibility. To enhance steganographic security, meanwhile addressing segmentation ambiguity, we propose a secure and disambiguating approach for linguistic steganography. In this letter, we focus on two questions: (1) Which candidate pools should be modified? (2) Which tokens should be retained? Firstly, we propose a secure token-selection principle that the sum of selected tokens' probabilities is positively correlated to statisti-cal imperceptibility. To meet both disambiguation and optimal security, we present a lightweight disambiguating approach that is finding out a maximum weight independent set (MWIS) in one candidate graph only when candidate-level ambiguity occurs. Experiments show that our approach outperforms the existing method in various security metrics, improving 25.7% statistical imperceptibility and 11.2% anti-steganalysis capacity averagely.