The rapid proliferation of Internet-of-Things (IoT) systems has led to increasingly heterogeneous, resource-constrained, and security-sensitive software deployments. In this context, detecting vulnerabilities in embedded and system-level IoT code has become particularly critical, as even a single exploitable flaw may compromise entire networks or devices. Vulnerability detection in real-world software continues to pose significant challenges due to the intricate nature of program semantics, the diversity of vulnerability patterns, and the limited explainability offered by current machine learning models. Although Large Language Models (LLMs) have exhibited remarkable capabilities in comprehending and reasoning about source code, their effectiveness is frequently compromised by inadequate domain knowledge and instances of hallucinated outputs. To address these limitations, we propose VulKnow, a retrieval-augmented framework for vulnerability detection that harnesses structured vulnerability knowledge alongside multi-stage prompt-based reasoning. Specifically, VulKnow first constructs a structured knowledge base derived from authoritative sources such as CWE/CVE reports and standard library specifications. Sub-sequently, it conducts context-aware knowledge retrieval and integrates the retrieved items into an LLM-based initial filtering module. To ensure high-confidence and verifiable results, we design a multi-stage reasoning pipeline that progressively validates vulnerabilities through semantic prompts and consistency checks. Experiments conducted on multiple real-world datasets (Linux, Qemu, Big-Vul) demonstrate that VulKnow significantly enhances precision, recall, and explainability compared to existing static analysis tools as well as LLM-only baselines. This positions VulKnow as a reliable solution for practical vulnerability auditing scenarios.

With the continuous development of the Industrial Internet of Things (IIoT) and cloud computing, an increasing number of firms store industrial data in the cloud. However, data outsourcing raises owners’ concerns about the confidentiality of their data, prompting them to encrypt data before uploading. When all files are encrypted, it becomes tough to locate specific ones. Searchable encryption enables users to retrieve needed data from encrypted datasets. Most existing keyword-searchable encryption schemes are designed for single-user scenarios; yet in practice, multiple users often access the same data, making single-user schemes unsuitable for multi-user environments. Additionally, sharing data on untrusted devices may lead to single key leakage. This paper presents a new searchable encryption scheme that supports multi-user search. By integrating blockchain, it addresses the problem of single key leakage. The scheme is proven to be impervious to keyword-guessing attacks and achieves trapdoor unlinkability, further enhancing data confidentiality. Performance evaluations compared with existing schemes demonstrate the proposed scheme’s efficiency and feasibility in IIoT deployments.

ABSTRACT Traditional codebooks used for tracing information leakage in text documents often suffer from limitations in embedding capacity, robustness, and efficiency due to their manual generation process. This paper proposes a robust text watermarking method based on the stroke components of Chinese characters. By designing an innovative approach, Chinese character strokes are divided into several distinct components, with only specific ones being selectively modified to generate new glyphs, thus forming a unique codebook. The watermark signals are embedded by substituting the carrier glyph with the newly generated one, and the signals are extracted using a template matching method. Experimental results demonstrate that, compared to traditional manually designed codebooks, the proposed method significantly reduces human labor and computational overhead while maintaining high visual quality. Moreover, it exhibits superior robustness and adaptability across various challenging scenarios, including digital noise attacks, print‐scanning attacks, and print‐camera capture, making it a highly effective solution for protecting textual information.

In Cloud-Assisted IoT (CAIoT), the large amounts of data generated by devices and sensors need to be shared and stored efficiently and securely. Broadcast proxy re-encryption (BPRE) technology ensures data privacy and enables efficient sharing. A significant issue with existing BPRE schemes is that the re-encryption permissions of the cloud server are uncontrolled, potentially leading to re-encryption operations without the consent of the data owner, and increasing the risk of data leakage. Additionally, most conditional proxy re-encryption (CPRE) schemes rely on computationally intensive bilinear pairing operations, making them unsuitable for resource-constrained IoT devices. To address this, this paper proposes a new certificateless conditional broadcast proxy re-encryption scheme, where the data owner sets conversion conditions when generating the original ciphertext and re-encryption keys, ensuring that only ciphertext meeting the conditions can being converted, thus preventing the cloud platform from abusing re-encryption permissions. Security analysis shows that the proposed scheme can resist chosen ciphertext attacks and collusion attacks in the random oracle model. Performance evaluation shows that the proposed scheme avoids bilinear pairing and hash-to-point operations, reducing the computational cost for the data owner. This improves computational efficiency, making it more suitable for CAIoT scenarios.

Online inspection of industrial parts becomes increasingly important for factories to improve production quality, where small sizes and high computations increase difficulties in the defect detection process. In order to solve these issues, we propose a defect detection model to identify detailed defects with edge computations, named fast attention segmentation classification network (FASCNet). In the model, we design skip connection attention (SCA) with edge average attention (eAA), edge sum attention (eSA), and attention for segmentation (AS) to catch complex features of extremely tiny defects. Additionally, global mixed pooling (GMP) operation is explored to adaptively obtain severe mapping into low dimensional feature domains. Furthermore, a tensor freeze decomposition (TFD) is discovered to reduce model computation and complexity for edge devices. Finally, we achieve an average precision (AP) of 97.86% and giga floating point operations (GFLOPs) of 64.4495 on the real-world sprocket surface data set, which has 16.06% of GFLOPs of the current state-of-the-art method while only lowering the AP by 0.58%. On the public data set, we achieve an AP of 98.83% and GFLOPs of 92.0379 on the Severstal Steel data set. The experimental results indicate that our model performs more effectively than other state-of-the-art approaches in terms of both accuracy and computational cost simultaneously.

As a secondary generation method, video recording will cause irreversible damage to the watermark within the video, which has always been challenging in video forensics. Although many video watermarking methods are reported in the literature, these methods, however, still cannot well resist camera recording. This has motivated the authors in this paper to introduce a new video watermarking method to resist camera recording. For the proposed method, two watermarks, i.e., copyright watermark and synchronization watermark, are embedded into the well-selected frequency domain coefficients. The synchronization watermark is used to ensure that the copyright watermark can be successfully extracted at the decoder side. To extract the copyright watermark without manual assistance, a neural network based segmentation model is applied to identify the watermarked video-playing region in the camera-recorded video. Meanwhile, automatic perspective correction is performed on the watermarked video-playing region so that the watermark information can be extracted accurately. The experiments show that the watermark data can be embedded into the raw video successfully and extracted from the camera-recorded video accurately by applying the proposed method. And, the proposed method significantly outperforms related works in terms of robustness in different scenarios, which has verified the superiority and applicability of the proposed method.

Abstract Data hiding aims to embed a secret message into a digital object such as image by slightly modifying the object content without arousing noticeable artefacts. The resultant object containing hidden information will be sent to a desired receiver via some insecure channels, e.g. images transmitted through noisy channel, social networks are vulnerable to unknown pollution or compression by a third party, which may lead the transmitted objects to be attacked such that the reconstructed message has a significant error rate. It therefore requires us to use robust embedding strategies for data hiding to realise reliable message retrieval. To this end, in this paper, a metric model to estimate the robustness of data hiding for noisy transmission based on the statistical characteristics of cover and embedding operation is presented, the former is mainly reflected by spatial frequency and texture feature, and the latter embedding operation is mainly reflected by embedding modification. The goal is to ensure that both statistical characteristics and embedding operation can be used to maximise the embedding robustness. To the best knowledge, it is the first time to estimate robustness before data hiding by a special metric model. Experimental results show that, by combining the proposed metric model in three classical data hiding methods, i.e. BPS, DE and QIM, the robustness can be significantly improved, which demonstrates its superiority and applicability.

With the increasing popularity of digital video communication, video data hiding has become an active research topic in covert communication and privacy protection. Traditional video data hiding methods often use quantized discrete cosine transform (QDCT) coefficients to carry a sufficient payload. However, since QDCT coefficients expose texture features and motion characteristics of the present video frame heavily, data embedding with QDCT coefficients may lead to significant intra-frame distortion and inter-frame distortion drift. To avoid obvious visual artifacts and keep bit-rate within a satisfactory level of the marked video, data embedding in QDCT coefficients should take into account both the intra-frame and inter-frame distortion impacts. It motivates the authors to propose an efficient cost assignment-based video data hiding method in this paper. The proposed cost assignment method aims to accurately evaluate the data embedding distortion. Specifically, the proposed scheme considers intra-frame changes and intra-frame distortion drift, for which the texture and motion changes of frames can be measured. The frame position is also used to reflect a cumulative distortion difference of multiple frames. For data embedding, syndrome-trellis code (STC) is adopted to minimize the overall distortion. Experimental results show that the proposed method significantly outperforms existing works in terms of payload-distortion performance.