Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://github.com/Nikphyc/SFDAH-MA.

The rapid increase in encrypted network traffic has made detecting malicious activities a critical challenge in network management, attracting significant research attention. However, most existing methods focus primarily on flow-based features, often neglecting the inherent structural heterogeneity and dynamic temporal variations in encrypted traffic, which limits their effectiveness in capturing the evolving nature of malicious activities. To address the aforementioned issue, we present a novel Dynamic Heterogeneous Information Network framework, DMT-DHIN, for Detecting Malicious Encrypted Traffic. DMT-DHIN constructs dynamic heterogeneous graphs by partitioning traffic into time slices, with nodes representing network entities (e.g., packets, protocols, IPs, and ports) and edges capturing their interactions. A heterogeneous graph attention mechanism effectively captures spatial dependencies among different node types. At the same time, the Transformer captures the temporal evolution of node features, enabling DMT-DHIN to identify dynamic patterns in encrypted traffic. We validate the effectiveness of DMT-DHIN by conducting evaluations on three datasets: CICIDS2017, USTC-TFC2016, and CICIoT2023. The results demonstrate that DMT-DHIN surpasses existing state-of-the-art methods, delivering marked enhancements in detection accuracy and F1-score, highlighting its superior ability to identify malicious encrypted traffic.

Unsupervised Cross-Modal Hashing (UCMH) models the intrinsic semantic correlations across different modalities to generate binary hash codes, facilitating efficient cross-modal retrieval. This technology offers notable advantages, such as independence from labeled data and superior generalization capabilities compared to supervised methods. However, most UCMH methods are designed for closed-set retrieval scenarios and have difficulty generalizing to open multi-modal data, which is common in real-world retrieval settings. This limitation hampers their performance in open retrieval tasks, particularly when these tasks involve novel categories. To address the above issue, we propose an Open-set Cross-Modal Hashing (OCMH) method, which enhances the generalization capability of trained UCMH models in an efficient plug-in manner for open cross-modal retrieval. Our method enables the model to learn from novel categories in open-set scenarios by increasing the pre-defined hash code length, while simultaneously preventing the catastrophic forgetting of trained knowledge from the closed-set domain using basic hash codes. Additionally, we introduce a historical-category detection module and an asymmetric optimization strategy to support the joint learning of basic and increased hash codes by replaying detected samples related to historical categories. By plugging our proposed method into several representative UCMH methods on three widely used datasets, experimental results show that the enhanced UCMH methods achieve superior retrieval performance in both open-set and closed-set scenarios. The source code is available at:https://github.com/WangBowen7/OCMH/.

Unsupervised domain adaptive hashing is a highly promising research direction within the field of retrieval. It aims to transfer valuable insights from the source domain to the target domain while maintaining high storage and retrieval efficiency. Despite its potential, this field remains relatively unexplored. Previous methods usually lead to unsatisfactory retrieval performance, as they frequently directly apply slightly modified domain adaptation algorithms to hash learning framework, or pursue domain alignment within the Hamming space characterized by limited semantic information. In this paper, we propose a simple yet effective approach named Comparative Prototype Hashing (CPH) for unsupervised domain adaptive image retrieval. We establish a domain-shared unit hypersphere space through prototype contrastive learning and then obtain the Hamming hypersphere space via mapping from the shared hypersphere. This strategy achieves a cohesive synergy between learning uniformly distributed and category conflict-averse feature representations, eliminating domain discrepancies, and facilitating hash code learning. Moreover, by leveraging dual-domain information to supervise the entire hashing model training process, we can generate hash codes that retain inter-sample similarity relationships within both domains. Experimental results validate that our CPH significantly outperforms the state-of-the-art counterparts across multiple cross-domain and single-domain retrieval tasks. Notably, on Office-Home and Office-31 datasets, CPH achieves an average performance improvement of 19.29% and 13.85% on cross-domain retrieval tasks compared to the second-best results, respectively. The source codes of our method are available at: https://github.com/christinecui/CPH.

The increasing complexity of cyber threats and the emergence of new attack technologies have brought huge challenges to attack incident analysis and source tracing. Using cyber threat intelligence to build a Cyber security Knowledge Graph (CKG) provides a new technical solution for attack attribution. Constructing a CKG requires numerous entity and relationship triples extracted from unstructured cyber threat intelligence texts. However, existing entity and relationship joint extraction methods in cyber threat intelligence face two problems. Firstly, they share the same word embeddings for both subtasks, ignoring the fine-grained semantic differences between the subtasks. Secondly, they rarely consider the interaction between the features of the two subtasks, which is vital for capturing the semantic dependencies between the tasks. To address these issues, we propose a joint entity and relationship extraction model specifically designed for network security concepts. We utilize two lightweight Fourier networks with independent weights to build a feature extraction module for encoding fine-grained features for entity recognition and relationship extraction tasks. Furthermore, we use a subtask feature interaction strategy assisted by a gated attention mechanism to enhance feature interaction between entity recognition and relationship extraction tasks. Use fine-grained entity recognition task information to guide relationship extraction to capture semantic dependencies between tasks. Experimental results on a cyber threat intelligence dataset demonstrate that our model outperforms existing baselines.

Unsupervised hashing has the desirable advantages of label independence, high storage, and retrieval efficiency, which is suitable for scalable image retrieval. Most existing methods focus on enhancing the image hashing model training process at the offline stage. However, little attention has been paid to the query content analysis by them at the online retrieval stage. They still suffer from important query semantic shortages, and thus limit the online retrieval performance, which is the ultimate objective of the image retrieval system. In this paper, we propose an Online Query Expansion Hashing (OQEH) for efficient image retrieval, by adaptively enhancing the discriminative capability of query hash codes in an expansion manner at the online retrieval stage. Specifically, we first design a self-expansion network to learn semantically invariant feature representations from images and their visual augmentations. Then, we conduct neighborhood-expansion to search similar samples for each image from a query expansion set with the semantically invariant features and design a Transformer architecture to adaptively transfer the semantics of neighbor samples to their corresponding images. With the support of semantically invariant features, query expansion set, and adaptive semantic transfer, the representation capability of query hash codes can be enhanced at the online retrieval stage. Experimental results demonstrate that the proposed OQEH method achieves superior retrieval accuracy and comparable retrieval efficiency compared with the state-of-the-art methods. Particularly, on MS COCO dataset, OQEH can obtain about 6% performance improvement compared with the state-of-the-art results. The source codes of our method are available at:https://github.com/christinecui/OQEH.

Although deep cross-modal hashing methods have shown superiorities for cross-modal retrieval recently, there is a concern about potential data privacy leakage when training the models. Federated learning adopts a distributed machine learning strategy, which can collaboratively train models without leaking local private data. It is a promising technique to support privacy-preserving cross-modal hashing. However, existing federated learning-based cross-modal retrieval methods usually rely on a large number of semantic annotations, which limits the scalability of the retrieval models. Furthermore, they mostly update the global models by aggregating local model parameters, ignoring the differences in the quantity and category of multi-modal data from multiple clients. To address these issues, we propose a Prototype Transfer-based Federated Unsupervised Cross-modal Hashing(PT-FUCH) method for solving the privacy leakage problem in cross-modal retrieval model learning. PT-FUCH protects local private data by exploring unified global prototypes for different clients, without relying on any semantic annotations. Global prototypes are used to guide the local cross-modal hash learning and promote the alignment of the feature space, thereby alleviating the model bias caused by the difference in the distribution of local multi-modal data and improving the retrieval accuracy. Additionally, we design an adaptive cross-modal knowledge distillation to transfer valuable semantic knowledge from modal-specific global models to local prototype learning processes, reducing the risk of overfitting. Experimental results on three benchmark cross-modal retrieval datasets validate that our PT-FUCH method can achieve outstanding retrieval performance when trained under distributed privacy-preserving mode. The source codes of our method are available at https://github.com/exquisite1210/PT-FUCH_P.

Recent studies have verified the success of deep hashing for efficient image retrieval. However, most existing methods require abundant human labeling data to optimize the large number of involved network parameters, which consequently restricts the scalability of deep image hashing. Alternatively, learning from freely available web images that inherently include rich semantics is a promising strategy. Nevertheless, the domain distribution gap will prevent transferring the semantics involved in the source web images to the target images. Besides, most existing deep image hashing methods suffer from excessive training time to achieve satisfactory performance without explicit supervision. How to efficiently train the deep image hashing network is another important problem that needs to be seriously considered. In this paper, we propose a Webly Supervised Image Hashing (WSIH) with a well-designed lightweight network. Our model enhances the semantics of unsupervised image hashing with the weak supervision from freely available web images, and simultaneously avoids involving over-abundant parameters in the deep network architecture. Particularly, we train a concept prototype learning network on the web images, learning well-trained network parameters and the prototype codes that hold the discriminative semantics of the potential visual concepts in target images. Further, we meticulously design a lightweight siamese network architecture and a dual-level transfer mechanism to efficiently translate the semantics learned from source web images to the target images. Experiments on two widely-tested image datasets show the superiority of the proposed method in both retrieval accuracy and training efficiency compared to state-of-the-art image hashing methods.The source codes of our method are available at: https://github.com/christinecui/WSIH.

Hashing learns compact binary codes to store and retrieve massive data efficiently. Particularly, unsupervised deep hashing is supported by powerful deep neural networks and has the desirable advantage of label independence. It is a promising technique for scalable image retrieval. However, deep models introduce a large number of parameters, which is hard to optimize due to the lack of explicit semantic labels and brings considerable training cost. As a result, the retrieval accuracy and training efficiency of existing unsupervised deep hashing are still limited. To tackle the problems, in this paper, we propose a simple and efficient Lightweight Augmented Graph Network Hashing (LAGNH) method with a two-pronged strategy. For one thing, we extract the inner structure of the image as the auxiliary semantics to enhance the semantic supervision of the unsupervised hash learning process. For another, we design a lightweight network structure with the assistance of the auxiliary semantics, which greatly reduces the number of network parameters that needs to be optimized and thus greatly accelerates the training process. Specifically, we design a cross-modal attention module based on the auxiliary semantic information to adaptively mitigate the adverse effects in the deep image features. Besides, the hash codes are learned by multi-layer message passing within an adversarial regularized graph convolutional network. Simultaneously, the semantic representation capability of hash codes is further enhanced by reconstructing the similarity graph. Experimental results show that our method achieves significant performance improvement compared with the state-of-the-art unsupervised deep hashing methods in terms of both retrieval accuracy and efficiency. Notably, on MS-COCO dataset, our method achieves more than 10% improvement on retrieval precision and 2.7x speedup on training time compared with the second best result.

Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due to its deep representation capability, fast retrieval speed and low storage cost. Particularly, unsupervised deep hashing has well scalability as it does not require any manually labelled data for training. However, owing to the lacking of label guidance, existing methods suffer from severe semantic shortage when optimizing a large amount of deep neural network parameters. Differently, in this paper, we propose aDual-level Semantic Transfer Deep Hashing(DSTDH) method to alleviate this problem with a unified deep hash learning framework. Our model targets at learning the semantically enhanced deep hash codes by specially exploiting the user-generated tags associated with the social images. Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes. On the one hand, instance-level semantics are directly preserved into hash codes from the associated tags with adverse noise removing. Besides, an image-concept hypergraph is constructed for indirectly transferring the latent high-order semantic correlations of images and tags into hash codes. Moreover, the hash codes are obtained simultaneously with the deep representation learning by the discrete hash optimization strategy. Extensive experiments on two public social image retrieval datasets validate the superior performance of our method compared with state-of-the-art hashing methods. The source codes of our method can be obtained athttps://github.com/research2020-1/DSTDH

Unsupervised deep hashing is a promising technique for large-scale image retrieval, as it equips powerful deep neural networks and has advantage on label independence. However, the unsupervised deep hashing process needs to train a large amount of deep neural network parameters, which is hard to optimize when no labeled training samples are provided. How to maintain the well scalability of unsupervised hashing while exploiting the advantage of deep neural network is an interesting but challenging problem to investigate. With the motivation, in this paper, we propose a simple but effective Inter-image Relation Graph Neural Network Hashing (IRGNNH) method. Different from all existing complex models, we discover the latent inter-image semantic relations without any manual labels and exploit them further to assist the unsupervised deep hashing process. Specifically, we first parse the images to extract latent involved semantics. Then, relation graph convolutional network is constructed to model the inter-image semantic relations and visual similarity, which generates representation vectors for image relations and contents. Finally, adversarial learning is performed to seamlessly embed the constructed relations into the image hash learning process, and improve the discriminative capability of the hash codes. Experiments demonstrate that our method significantly outperforms the state-of-the-art unsupervised deep hashing methods on both retrieval accuracy and efficiency.

Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods have greatly boosted the retrieval performance, under the strong supervision of large amounts of manually annotated semantic labels. However, their performance is highly dependent upon the supervised labels, which significantly limits the scalability. In contrast, unsupervised deep hashing without label dependence enjoys the advantages of well scalability. Nevertheless, due to the relaxed hash optimization, and more importantly, the lack of semantic guidance, existing methods suffer from limited retrieval performance. In this paper, we propose a SCAlable Deep Hashing (SCADH) to learn enhanced hash codes for social image retrieval. We formulate a unified scalable deep hash learning framework which explores the weak but free supervision of discriminative user tags that are commonly accompanied with social images. It jointly learns image representations and hash functions with deep neural networks, and simultaneously enhances the discriminative capability of image hash codes with the refined semantics from the accompanied social tags. Further, instead of simple relaxed hash optimization, we propose a discrete hash optimization method based on Augmented Lagrangian Multiplier to directly solve the hash codes and avoid the binary quantization information loss. Experiments on two standard social image datasets demonstrate the superiority of the proposed approach compared with stateof- the-art shallow and deep hashing techniques.