Various malicious activities performed by the social bots have brought a crisis of trust to the online social networks. In this paper, we propose a social bot detection method, named Accou2vec, based on community walk. First, in order to cut off the attacking edges between the human and bot accounts, the deep autoencoder-like non-negative matrix factorization community detection algorithm is leveraged to divide the social graph into multiple subgraphs. Then, we design the community walk rule that controls the intra-community walk and inter-community walk differently, considering both the number of nodes and edges in the community. Subsequently, the graph representation learning is used to learn the representation vector of each account. Finally, the representation vectors of labeled social bots and human accounts are used to train the classifier for social bots detection. Extensive experimental results on two real-world datasets show the superior performance of the proposed method over the state-of-the-art.

Covert communication has numerous applications across various domains, including the military, the Internet of Things, blockchain, and beyond. Among these, the covert communication method that exploits the unique cover types inherent in single-player games has garnered substantial attention from researchers. However, existing methods are unsuitable for the current social media such as audio and video and traditional blogs, poor interactivity, and player communication content can easily cause unnatural behavior of game characters. For this reason, a covert communication framework based on live broadcast bullet comment games is proposed. Within the framework, the sender initially applies arithmetic encoding to compress the confidential message. Then, the processed message is transformed into the content and time attributes of the bullet comment, adhering to the game's rules. Finally, these bullet comments are sent to the designated live broadcast game room. The receiver extracts the bullet comments sent by the sending party in real time and converts them into confident messages. As a representative of the framework,${\mathit{Plants vs. Zombies}}$, is utilized as an example. Experimental results have identified the optimal method for sending game bullet comments, balancing detection resistance and transmission speed. The proposed approach offers notable advantages in security interactivity and transmission speed, with 4.1 times faster than existing processes.

Social bot detection plays a crucial role in enabling social media platforms and governments to effectively manage and regulate social networks. Existing methods, which typically rely on multi-modal feature fusion, often concentrate on interactions between paired accounts and their immediate neighbors, overlooking the dynamic evolution of social networks over time. To address this gap, we propose a community-aware spatio-temporal hypergraph contrastive learning method for social bot detection, namely BotSTHCL. Specifically, we construct a temporal community-aware hypergraph and apply a contrastive learning framework in a semi-supervised setting, facilitating the effective extraction and representation of node features. Our model not only captures interactions among multiple accounts within a community but also accounts for the evolving nature of social networks. Extensive experiments on publicly available datasets demonstrate the effectiveness and superiority of our approach. The code is available at https://github.com/FengLiuii/BotSTHCL.

Various malicious activities performed by social bots have brought a crisis of trust to online social networks. Existing social bot detection methods often overlook the significance of community structure features and effective fusion strategies for multimodal features. To counter these limitations, we propose BotCF, a novel social bot detection method that incorporates community features and utilizes cross-attention fusion for multimodal features. In BotCF, we extract community features using a community division algorithm based on deep autoencoder-like non-negative matrix factorization. These features capture the social interactions and relationships within the network, providing valuable insights for bot detection. Furthermore, we employ cross-attention fusion to integrate the features of the account’s semantic content, properties, and community structure. This fusion strategy allows the model to learn the interdependencies between different modalities, leading to a more comprehensive representation of each account. Extensive experiments conducted on three publicly available benchmark datasets (Twibot20, Twibot22, and Cresci-2015) demonstrate the effectiveness of BotCF. Compared to state-of-the-art social bot detection models, BotCF achieves significant improvements in accuracy, with an average increase of 1.86%, 1.67%, and 0.47% on the respective datasets. The detection accuracy is boosted to 86.53%, 81.33%, and 98.21%, respectively.

Social bot detection methods using graph neural networks (GNNs) are thriving, but the structural complexity of GNN also brings more training costs on large-scale data and interpretability concerns. In this paper, we propose a social bot detection method, BotCS, which utilizes both the attribute and the structural features of the social graph at a smaller computational cost than GNN-based detection methods. BotCS makes a base prediction with a simple multilayer perceptron classifier (MLP) and then propagates the classification residuals of the training set to other nodes for further correction. Then, it smooths the corrected prediction by label propagation. With little end-to-end training, this course is low-cost and scalable. We analyze the local interaction pattern between bots and human users, and designed the corresponding residual propagation and smoothing rules from the local perspective, which ensures the interpretability of BotCS. Experimental results show that BotCS achieves similar detection results to state-of-the-art methods with one or two orders of magnitude fewer parameters.

Abstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments.

With the rapid development of Internet technologies, an increasing amount of auxiliary data can be readily obtained through Web services. To alleviate the data sparsity issue, auxiliary data based recommendation has emerged for better recommendation performance. However, existing auxiliary data based methods suffer from two problems. First, only the relation features related to the meta-paths are extracted from auxiliary data, which may lead to features useful for recommendation being lost irreversibly. Second, an assumption is made that an individual has the same preference over the identical characteristic of different items, which is often invalid and may lead to misleading recommendations. Actually, a user may place different importance on the same feature of different items, and an item may get different attention from the same feature of different users. In this paper, we propose a neural network framework, named Neural Attention Recommendation model (NARec), for auxiliary data based collaborative filtering. For the first problem, we characterize users and items from three aspects, namely latent features, attribute features, and meta-path based relation features, which can comprehensively extract the useful recommendation features from auxiliary data. Regarding the second problem, we integrate different user features and item features into an attention mechanism based rating prediction model for recommendation, which can adaptively characterize the personalized features of users and items. Extensive experiments on three real-world datasets demonstrate that NARec significantly outperforms the state-of-the-art recommendation methods in the rating prediction task.

Abstract To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, a self‐embedding fragile image watermarking algorithm based on deneighbourhood mapping are proposed. First, the image is divided into several 2 × 2 blocks, and authentication watermark and recovery watermark are generated based on the average value of the image blocks. Then, the denighbourhood mapping is implemented as, for each image block, its mapping block is randomly selected outside its neighbourhood. Finally, the authentication watermark and the recovery watermark are embedded into the image block itself and its mapping block. Theoretical analysis indicates that in the case of continuous area tampering, the proposed watermarking algorithm can achieve a better recovery rate than that of the method based on the random mapping. The experimental results verify the rationality and effectiveness of the theoretical analysis. Moreover, compared with the existing embedding algorithms based on random mapping, chaos mapping, and Arnold mapping, in the case of continuous area tampering, the proposed algorithm also achieves a higher average recovery rate.

While 3-D steganography and digital watermarking represent methods for embedding information into 3-D objects, 3-D steganalysis aims to find the hidden information. Previous research studies have shown that by estimating the parameters modeling the statistics of 3-D features and feeding them into a classifier we can identify whether a 3-D object carries secret information. For training the steganalyzer, such features are extracted from cover and stego pairs, representing the original 3-D objects and those carrying hidden information. However, in practical applications, the steganalyzer would have to distinguish stego-objects from cover-objects, which most likely have not been used during the training. This represents a significant challenge for existing steganalyzers, raising a challenge known as the cover source mismatch (CSM) problem, which is due to the significant limitation of their generalization ability. This paper proposes a novel feature selection algorithm taking into account both feature robustness and relevance in order to mitigate the CSM problem in 3-D steganalysis. In the context of the proposed methodology, new shapes are generated by distorting those used in the training. Then a subset of features is selected from a larger given set, by assessing their effectiveness in separating cover-objects from stego-objects among the generated sets of objects. Two different measures are used for selecting the appropriate features: 1) the Pearson correlation coefficient and 2) the mutual information criterion.

3D steganalysis aims to find the changes embedded through steganographic or information hiding algorithms into 3D models. This research study proposes to use new 3D features, such as the edge vectors, represented in both Cartesian and Laplacian coordinate systems, together with other steganalytic features, for improving the results of 3D steganalysers. In this way the local feature vector used by the steganalyzer is extended to 124 dimensions. We test the performance of the extended local feature set, and compare it to four other steganalytic features, when detecting the stego-objects watermarked by six information hiding algorithms.

3D steganography is used in order to embed or hide information into 3D objects without causing visible or machine detectable modifications. In this paper we rethink about a high capacity 3D steganography based on the Hamiltonian path quantization, and increase its resistance to steganalysis. We analyze the parameters that may influence the distortion of a 3D shape as well as the resistance of the steganography to 3D steganalysis. According to the experimental results, the proposed high capacity 3D steganographic method has an increased resistance to steganalysis.

Steganalysis aims to identify those changes performed in a specific media with the intention to hide information. In this paper we assess the efficiency, in finding hidden information, of several local feature detectors. In the proposed 3D steanalysis approach we first smooth the cover object and its corresponding stego-object obtained after embedding a given message. We use various operators in order to extract local features from both the cover and stego-objects, and their smoothed versions. Machine learning algorithms are then used for learning to discriminate between those 3D objects which are used as carriers of hidden information and those are not used. The proposed 3D steganalysis methodology is shown to provide superior performance to other approaches in a well known database of 3D objects.

This paper introduces a novel method for extracting sets of feature from 3D objects characterising a robust steganalyzer. Specifically, the proposed steganalyzer should mitigate the Cover Source Mismatch (CSM) paradigm. A steganalyzer is considered as a classifier aiming to identify separately cover and stego objects. A steganalyzer behaves as a classifier by considering a set of features extracted from cover stego pairs of 3D objects as inputs during the training stage. However, during the testing stage, the steganalyzer would have to identify whether specific information was hidden in a set of 3D objects which can be different from those used during the training. Addressing the CSM paradigm corresponds to testing the generalization ability of the steganalyzer when introducing distortions in the cover objects before hiding information through steganography. Our method aims to select those 3D features that model best the changes introduced in objects by steganography or information hiding and moreover they are able to generalize for different objects, not present in the training set. The proposed robust steganalysis approach is tested when considering changes in 3D objects such as those produced by mesh simplification and additive noise. The results obtained from this study show that the steganalyzers trained with the selected set of robust features achieve better detection accuracy of the changes embedded in the objects, when compared to other sets of features.

In order to achieve higher estimation accuracy of the embedding change rate of a stego object, an ensemble learning-based estimation method is presented. First of all, a framework of embedding change rate estimation based on estimator ensemble is proposed. Then an algorithm of building the estimator ensemble, the core of the framework, is concretely described. Finally, a pruning method for estimator ensemble is proposed in consideration of both the diversity among the base estimators and accuracy of each of them. The experimental results for three modern steganographic algorithms (nsF5, PQ and PQt) indicate that the proposed method acquired better performance than the existed typical method. Furthermore, the pruned estimator ensemble with less base estimators maintained, even slightly improved the estimation accuracy, compared to the one without purning.