Steganographic threats are widespread in social networks, particularly on social media platforms. Existing methods mainly rely on statistical features and social network graph-based modeling, but struggle under sparsity and fragmentation due to limited semantics and weak graph connections that fail to capture deep inter-text dependencies. Therefore, we propose the CASS-LS framework, a Context-Aware and Semantic-Synergistic Linguistic Steganalysis framework for social network. CASS-LS consists of three key modules. The Semantic-Aware Feature Pretraining (SAFP) module generates more discriminative semantic representations to enhance the expressiveness of fragmented texts. The Semantic Synergy and Context-Aware Modeling (SSCA) module strengthens graph-based modeling to reconstruct inter-text relationships and capture long-range dependencies among sparse texts. The Semantics-Context-Synergistic Contrastive Loss Supervision (SCS-CLS) module compares semantic and contextual similarities between text pairs, improving model performance in detecting weak steganographic signals. Extensive experiments demonstrate that CASS-LS achieves superior F1 performance in social network steganalysis tasks.

Provably secure steganography ensures indistinguishability between stego and cover carrier through mathematical proofs. However, existing methods face limited embedding capacity and distribution synchronization challenges, especially at high embedding rates. To address these issues, we propose TASDF-Stega, a text-audio joint steganography method based on the latent space of diffusion models, which achieves high capacity and provable security. First, we design an encrypted steganographic mapping module with adaptive arithmetic decoding, which efficiently embeds secret information into the latent space while preserving the distribution. Second, a reversible secret diffusion mechanism enables high-capacity embedding and precise extraction. Moreover, to resolve the problem of distribution parameter synchronization in practical communication, we introduce an audio-assisted joint encode module. This design ensures accurate reconstruction of the diffusion inverse process and avoids cumulative extraction errors. Experimental results on multiple datasets demonstrate that TASDF-Stega achieves provable security, the outperforms state-of-the-art methods in embedding capacity and imperceptibility.

Current linguistic steganography techniques have achieved promising results in terms of resistance to steganalysis. However, they still face significant challenges in maintaining robustness against various forms of attacks, particularly paraphrasing attacks. To address this issue, this paper proposesKeyword-basedRobust LinguisticSteganography against paraphrasing attacks (KR-stega). First, to improve robustness, we leverage keywords as robust features of sentences while introducing locality-sensitive hashing (LSH) to partition the semantic space of words. By generating sentences conditioned on keywords, the method ensures accurate extraction of the secret message. Second, to improve imperceptibility, this paper introduces an imperceptible-enhancing sampling strategy. This strategy enhances the diversity of stegotext, thereby significantly lowering the risk of detection by steganalysis models. Experimental results demonstrate that this method not only enhances the robustness of stegotext against paraphrasing attacks, but also effectively improves the capability to resist steganalysis attacks.

Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interest to match candidate news. Recent efforts have primarily focused on extracting local subgraph information in a global click graph constructed by the clicked news sequence of all users. However, the computational complexity of extracting global click graph information has hindered the ability to utilize far-reaching linkage which is hidden between two distant nodes in global click graph collaboratively among similar users. To overcome the problem above, we propose a Global-view Long Chain Interests Modeling for news recommendation (GLoCIM), which combines neighbor interest with long chain interest distilled from a global click graph, leveraging the collaboration among similar users to enhance news recommendation. We therefore design a long chain selection algorithm and long chain interest encoder to obtain global-view long chain interest from the global click graph. We design a gated network to integrate long chain interest with neighbor interest to achieve the collaborative interest among similar users. Subsequently we aggregate it with local news category-enhanced representation to generate final user representation. Then candidate news representation can be formed to match user representation to achieve news recommendation. Experimental results on real-world datasets validate the effectiveness of our method to improve the performance of news recommendation.

Linguistic steganalysis often encounters domain shift in practice, mainly due to differences in text sources and steganographic methods.These variations create distribution discrepancies between the training (source domain) and test (target domain) sets, reducing detection accuracy.Most existing domain adaptation methods for linguistic steganalysis rely on labeled source domain data to alleviate these issues, but due to data privacy or transmission costs, source domain data is often unavailable in many real-world scenarios.Without access to this data, models cannot directly compare feature distributions between the source and target domains, hindering the model's ability to learn the target domain's features and ultimately affecting detection performance for stego texts.In this paper, we propose a Clustering-driven Pseudo-labeling method for Source-free domain adaptation in Linguistic Steganalysis (CPSLS).During the adaptation phase, we leverage the clustering structure of the target domain data to generate pseudo-labels, helping the model identify stego features in the target domain.Additionally, we use a weighted classification loss function to reduce the impact of incorrect pseudo-labels.To prevent the model from overlooking the diversity between stego and cover texts during optimization, we introduce a prediction diversity loss, improving the model's ability to differentiate between the two.Experimental results show that CPSLS not only has stronger practical applicability but also outperforms existing domain adaptation linguistic steganalysis in terms of detection accuracy.

To circumvent the unbridled and ever-encroaching surveillance and censorship in cyberspace, steganography has garnered attention for its ability to hide private information in innocent-looking carriers. Current provably secure steganography approaches require a pair of encoder and decoder to hide and extract private messages, both of which must run the same model with the same input to obtain identical distributions. These requirements pose significant challenges to the practical implementation of steganography, including limited access to powerful hardware and the intolerance of any changes to the shared input. To relax the limitation of hardware and solve the challenge of vulnerable shared input, a novel and practically significant scenario with asymmetric resource should be considered, where only the encoder is high-resource and accessible to powerful models while the decoder can only read the stegano-graphic carriers without any other model's input. This paper proposes a novel provably robust and secure steganography framework for the asymmetric resource setting. Specifically, the encoder uses various permutations of distribution to hide secret bits, while the decoder relies on a sampling function to extract the hidden bits by guessing the permutation used. Further, the sampling function only takes the steganographic carrier as input, which makes the decoder independent of model's input and model itself. A comprehensive assessment of applying our framework to generative models substantiates its effectiveness. Our implementation demonstrates robustness when transmitting over binary symmetric channels with errors.

Linguistic steganalysis aims to distinguish stego text from cover text. However, most existing methods heavily rely on a large number of stego text samples for training. In real-world scenarios, the cover text is far more abundant than the stego text, making it extremely difficult to obtain sufficient stego text for training. Furthermore, the scarcity of stego text also increases the difficulty of detection, posing greater challenges for steganalysis. In contrast, cover text is relatively easier to obtain in real-world scenarios, but current methods fail to fully utilize this resource. In this paper, we propose a Domain-Assisted Few-shot linguistic steganalysis method called DAF-Stega. To make full use of the cover text, we incorporate cover texts from multiple domains to assist in training. To address the scarcity of stego texts, we perform few-shot steganalysis based on a small amount of stego text and employ dynamic decision-making to generate pseudo-labels for self-training, enhancing model performance. Experimental results show that in few-shot learning scenarios, DAF-Stega effectively addresses the steganalysis problem under uncertain stego text proportions and outperforms existing methods.

Linguistic steganalysis faces the challenge of increasingly high-quality stego text, making it difficult to distinguish these text from cover text. The two main issues with current methods are: 1) deep learning models tend to overfit, which hurts their ability to apply to new situations, and 2) feature fusion models don't mix different types of features effectively, leading to poor results. In this letter, we propose aTextDualAttention linguistic steganalysis methodFusingStatistical andMulti-layerSemantic features (TDA-FSMS). TDA-FSMS firstly extracts multi-layer semantic features using different encoder layers from Enhanced Representation through knowledge integration (ERNIE), combining shallow and deep features to relieve potential overfitting. TDA-FSMS also designs a text dual attention network that simultaneously maps both multi-layer semantic and statistical features into a shared high-dimensional space to bring in a smooth feature fusion. Experimental results show that the text dual attention and the multi-layer semantic fusion enable TDA-FSMS to improve steganalysis performance than existing methods.

Recent advancements in deep learning have significantly improved linguistic steganalysis, but challenges persist when labeled samples are scarce in the target domain. Existing cross-domain linguistic steganalysis methods seek to improve model generalization by minimizing the domain discrepancy between the source and target domains. However, these steganalysis methods often struggle with incorrect alignment between stego and cover texts in both domains, which hampers the generalization of steganalysis models. Additionally, they struggle to capture domain-specific features of the target domain, reducing the effectiveness of steganalysis models in discriminating stego texts. To address these issues, we propose a novel Class-aware Adversarial unsupervised Domain Adaptation (CADA) method, which operates in two stages. In the first stage, Class-aware Adversarial Pre-Training (CAPT), we design the Weighted Class-Aware Domain Distance (WCADD) to leverage class information of stego and cover texts. This ensures accurate class-aware alignment across domains. In the CAPT stage, the steganalysis model is pre-trained with WCADD, Class-Aware Adversarial Training (CAAT), and Class-Aware Label Smoothing (CALS) to enhance its ability to extract domain-invariant features, thereby improving its generalization. In the second stage, Class-aware Fine-Tuning (CFT), we employ the pre-trained steganalysis model alongside the Class-Aware Progressive Strategy (CAPS) to generate pseudo-labels for the target domain. Fine-tuning the model with these pseudo-labels enhances its ability to recognize domain-specific features, thereby improving its performance in discriminating stego texts within the target domain. Extensive experiments demonstrate that our proposed method outperforms the existing baseline methods.

Face forgery instances involving celebrities are on the rise, owing to the ease with which their large quantity of videos may be accessible on the Internet, world leaders particularly. While current face manipulation detectors have achieved impressive results on several open datasets, which incorporate persons with various identities, they show performance degradation on these high-quality ones targeting at celebrities. What is more, these online videos usually undergo compression processing, marking the detection task harder. Besides, more face manipulation techniques arise for celebrities other than face-swap, such as lip-synchronize and image-animation, with which most works have not been concerned. This paper proposes a dual stream learning facial and speaking patterns method to protect celebrities against deepfakes. We design an action unit module based on facial action coding system along with anAction Unit Transformer(AUT) to exploit facial expressions embeddings. Besides, our method's dual stream architecture utilizes aTemporal Convolutional Network(TCN) to extract lip motion pattern and learns the relatedness between facial and speaking patterns. Our method could protect the person of interest (POI) against deepfakes in an end-to-end manner. Extensive experiments show that our method achieves better performance and has a higher resistance to video compression than state-of-the-art detection models.

Convolutional Neural Networks (CNN) based detectors perform well in face manipulation detection, but are still limited by redundant information. Some methods focus on blending boundary to localize manipulation regions, discarding a part of useless information like background of image. But these methods still contain deceptive information such as facial regions without texture, which occupies resources and affects detection accuracy. Besides, these methods left out some features useful for identification. Therefore, this paper proposes a module by conducting semantic masks to guide detectors focus on face. The semantic segmentation masks focus on the facial features such as hair, eyes and other important areas, which can offer effective face identification high level semantic features. Our method uses masks as an attention-based data augmentation module and is simple for many DeepFake detection models to integrate. Experiments on multiple detectors with and without our module show our module's effectiveness. Without modifying their structural design, our approach enables CNN-based detectors to perform better. Especially, our method is well-suited for protecting the person of interest against face forgery.

Heterogeneous parallel steganography (HPS) has become a new trend of current streaming media voice steganography, which hides secret information in the frames of streaming media with multiple kinds of orthogonal steganography. Because of the complexity and imperceptibility of HPS, detecting its existence is a challenge for previous steganalysis methods, especially in the case of short sliding window length and low embedding rate. In order to improve the situation, we design a fast and efficient detection method named the key feature extraction and fusion network (KFEF) based on attention mechanism. The proposed model is able to effectively extract the key characteristic of the exceptions due to steganography and fuse the extracted features for different steganographic algorithms used in HPS. Experimental results show that the proposed method significantly improves the classification accuracy in detecting both low embedding rate samples and short segment samples. In addition, the detection time consumption is shorter than other methods and meets real-time requirements. Finally, with the help of attention we can predict the approximate locations of secret information which may bring new ideas to further steganalysis.

Abstract With increasing number of cloud data leakage accidents exposed, outsourced data control becomes a more and more serious concern of their owner. To relieve the concern of these cloud users, reliable logging schemes are widely used to generate proof for data confidentiality auditing. However, high frequency operation and fine operation granularity on cloud data both result in a considerably large volume of operation logs, which burdens communication and computation in log auditing. This paper proposes a multi-grained log auditing scheme to make logs volume smaller and log auditing more efficient. We design a logging mechanism to support multi-grained data access with Merkle Hash Tree structure. Based on multi-grained log, we present a log auditing approach to achieve data confidentiality auditing and leakage investigation by making an Access List. Experiments results indicate that our scheme obtains about 54% log volume and 60% auditing time of fine-grained log auditing scheme in our scenario.

Data accountability can record and track data usage in cloud storage, in order to cope with users' fear of losing control of their own data or even data leakage. However, logs recording unauthorized data access could be omitted or falsified in untrusted cloud storage, which is the critical factor of unreliable data accountability. To address this problem, in this paper, we propose a novel Cloud Data Accountability Framework to ensure reliable logging for data accountability. In particular, we adopt programmable Java JAR file coupling with data to enclose access policy. This mechanism ensures that data access through JAR will trigger authentication and automated logging local to the JAR. To prevent data access without JAR and protect data from key abuse attack, we provide JAR-based Data Access Protocol. Extensive security and performance analysis makes comparison between our logging mechanism and the state-of-the-art. Results indicate that the proposed mechanism is more reliable and achieves space and time efficiency.