Search Results (307)

Java Management Extensions (JMX) are indispensable for managing and administrating Java software solutions, yet when exposed through HTTP bridges such as Jolokia they can radically enlarge an application’s attack surface. This paper presents the first in-depth analysis of CVE-2022-41678, a vulnerability discovered by the authors in Apache ActiveMQ that combines Jolokia’s remote JMX access with Log4J2 management beans to achieve full remote code execution. Using a default installation testbed, we enumerate the Log4J MBeans surfaced by Jolokia, demonstrate arbitrary file read, file write, and server-side request–forgery primitives, and finally to leverage the file write capabilities to obtain a shell, all via authenticated HTTP(S) requests only. The end-to-end exploit chain requires no deserialization gadgets and is unaffected by prior Log4Shell mitigations. We have also automated the entire exploit process via proof-of-concept scripts on a stock ActiveMQ 5.17.1 instance. We discuss the broader security implications for any software exposing JMX-managed or Jolokia-managed Log4J contexts, provide concrete hardening guidelines, and outline design directions for safer remote-management stacks. The findings underscore that even “benign” management beans can become critical when surfaced through ubiquitous HTTP management gateways. Full article

COPA is a notable authenticated online cipher and was one of the winning proposals for the CAESAR competition. Current works describe how to break the existentially unforgeable under quantum chosen message attack (EUF-qCMA) of COPA. However, these works do not demonstrate the confidentiality of COPA in the quantum setting. This paper fills this gap, considers the indistinguishable under quantum chosen-plaintext attack (IND-qCPA) security for privacy, and presents the first IND-qCPA security analysis of COPA. In addition, in order to effectively avoid the problems of quantum existential forgery attack and quantum distinguishing attack, we introduce an intermediate state doubling-point technology into COPA, restrict the associated data non-emptiness, and present an enhanced variant, called COPA-ISDP, to support the IND-qCPA and EUF-qCMA security. Our work is of great significance, as it provides a simple and effective post-quantum secure design idea to resist Simon’s attack. Full article

This article develops a method for analysing sensor data to prevent cyberattacks using a modified LSTM network. This method development is based on the fact that in the context of the rapid increase in sensor devices used in critical infrastructure, it is becoming an urgent task to ensure these systems’ security from various types of attacks, such as data forgery, man-in-the-middle attacks, and denial of service. The method is based on predicting normal system behaviour using a modified LSTM network, which allows for effective prediction of sensor data because the F1 score = 0.90, as well as on analysing anomalies detected through residual values, which makes the method highly sensitive to changes in data. The main result is high accuracy of attack detection (precision = 0.92), achieved through a hybrid approach combining prediction with statistical deviation analysis. During the computational experiment, the developed method demonstrated real-time efficiency with minimal computational costs, providing accuracy up to 92% and recall up to 89%, which is confirmed by high AUC = 0.94 values. These results show that the developed method is effectively protecting critical infrastructure facilities with limited computing resources, which is especially important for cyber police. Full article

Pairing-free certificateless signature (PF-CLS) schemes are ideal authentication solutions for resource-constrained environments like the Internet of Things (IoT) due to their low computational, storage, and communication resource requirements. However, it has come to light that many PF-CLS schemes are vulnerable to forged signature attacks. In this paper, we use a novel attack method to prove that a class of PF-CLS schemes with the same security vulnerabilities cannot resist Type I adversary attacks, and we find that, even if some schemes are improved to invalidate existing attack methods, they still cannot defend against the new attack method proposed in this paper. Subsequently, we introduce an enhanced scheme proven to be resilient against both types of adversary attacks under the random oracle model (ROM). Performance analysis shows that, compared with several existing PF-CLS schemes, our scheme offers higher computational efficiency. Full article

Industrial Control Systems (ICSs) are fundamental to critical infrastructure, yet they face increasing cybersecurity threats, particularly data integrity attacks like replay and data forgery attacks. Traditional IT-centric security measures are often inadequate for the Operational Technology (OT) environment due to stringent real-time and reliability requirements. This paper proposes an endogenous security defense mechanism based on the Luenberger observer and residual analysis. By embedding a mathematical model of the physical process into the control system, this approach enables real-time state estimation and anomaly detection. We model the ICS using a linear state-space representation and design a Luenberger observer to generate a residual signal, which is the difference between the actual sensor measurements and the observer’s predictions. Under normal conditions, this residual is minimal, but it deviates significantly during a replay attack. We formalize the system model, observer design, and attack detection algorithm. The effectiveness of the proposed method is validated through a simulation of an ICS under a replay attack. The results demonstrate that the residual-based approach can detect the attack promptly and effectively, providing a lightweight yet robust solution for enhancing ICS security. Full article

As information technology advances, digital content has become widely adopted across diverse fields such as news broadcasting, entertainment, commerce, and forensic investigation. However, the availability of sophisticated multimedia editing tools has significantly increased the risk of video and image forgery, raising serious concerns about content authenticity at both societal and individual levels. To address the growing need for robust and accurate detection methods, this study proposes a novel video forgery detection model that integrates both spatial and frequency-domain features. The model is built on a ResNet-LSTM framework enhanced by a Convolutional Block Attention Module (CBAM) for spatial feature extraction, and further incorporates Discrete Cosine Transform (DCT) to capture frequency domain information. Comprehensive experiments were conducted on several mainstream benchmark datasets, encompassing a wide range of forgery scenarios. The results demonstrate that the proposed model achieves superior performance in distinguishing between authentic and manipulated videos. Additional ablation and comparative studies confirm the contribution of each component in the architecture, offering deeper insight into the model’s capacity. Overall, the findings support the proposed approach as a promising solution for enhancing the reliability of video authenticity analysis under complex conditions. Full article

As face forgery techniques, particularly the DeepFake method, progress, the imperative for effective detection of manipulations that enable hyper-realistic facial representations to mitigate security threats is emphasized. Current spatial domain approaches commonly encounter difficulties in generalizing across various forgery methods and compression artifacts, whereas frequency-based analyses exhibit promise in identifying nuanced local cues; however, the absence of global contexts impedes the capacity of detection methods to improve generalization. This study introduces a hybrid architecture that integrates Efficient-ViT and multi-level wavelet transform to dynamically merge spatial and frequency features through a dynamic adaptive multi-branch attention (DAMA) mechanism, thereby improving the deep interaction between the two modalities. We innovatively devise a joint loss function and a training strategy to address the imbalanced data issue and improve the training process. Experimental results on the FaceForensics++ and Celeb-DF (V2) have validated the effectiveness of our approach, attaining 97.07% accuracy in intra-dataset evaluations and a 74.7% AUC score in cross-dataset assessments, surpassing our baseline Efficient-ViT by 14.1% and 7.7%, respectively. The findings indicate that our approach excels in generalization across various datasets and methodologies, while also effectively minimizing feature redundancy through an innovative orthogonal loss that regularizes the feature space, as evidenced by the ablation study and parameter analysis. Full article

Museums may acquire archaeological artefacts discovered by non-specialists or amateur archaeologists, holding the potential to promote the safeguarding of cultural heritage by integrating the local community in their activities. However, this also creates an opportunity for the fraudulent sale of modern forgeries presented as archaeological artefacts, resulting in the need for a critical assessment of the artefact’s authenticity prior to acquisition by the museum. In 2019, the regional museum in Cáceres (Spain) was offered the opportunity to acquire a Phoenician-Punic head pendant, allegedly discovered in the vicinity of the city. The artefact’s authenticity was assessed by traditional approaches, including typological analysis and analysis of manufacture technique, which raised doubts about its purported age. VP-SEM-EDS analysis of the chemical composition of the different glass portions comprising the pendant was used for non-invasive determination of glassmaking recipes, enabling the identification of glass components incompatible with known Iron Age glassmaking recipes from the Mediterranean. Further comparison with historical and modern glassmaking recipes allowed for the identification of the artefact as a recent forgery made from glasses employing modern colouring and opacifying techniques. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, we address this challenge by leveraging both local texture cues and global frequency domain information in a complementary manner to enhance the robustness of face forgery detection. Specifically, we introduce a local texture mining and enhancement module. The input image is segmented into patches and a subset is strategically masked, then texture enhanced. This joint masking and enhancement strategy forces the model to focus on generalizable localized texture traces, mitigates overfitting to specific identity features and enabling the model to capture more meaningful subtle traces of forgery. Additionally, we extract multi-scale frequency domain features from the face image using wavelet transform, thereby preserving various frequency domain characteristics of the image. And we propose an innovative frequency-domain processing strategy to adjust the contributions of different frequency-domain components through frequency-domain selection and dynamic weighting. This Facilitates the model’s ability to uncover frequency-domain inconsistencies across various global frequency layers. Furthermore, we propose an integrated framework that combines these two feature modalities, enhanced with spatial attention and channel attention mechanisms, to foster a synergistic effect. Extensive experiments conducted on several benchmark datasets demonstrate that the proposed technique demonstrates superior performance and generalization capabilities compared to existing methods. Full article

The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial–frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment. Full article

Most image forgery localization methods rely on supervised learning, requiring large labeled datasets for training. Recently, several unsupervised approaches based on the variational autoencoder (VAE) framework have been proposed for forged pixel detection. In these approaches, the latent space is built by a simple Gaussian distribution or a Gaussian Mixture Model. Despite their success, there are still some limitations: (1) A simple Gaussian distribution assumption in the latent space constrains performance due to the diverse distribution of forged images. (2) Gaussian Mixture Models (GMMs) introduce non-convex log-sum-exp functions in the Kullback–Leibler (KL) divergence term, leading to gradient instability and convergence issues during training. (3) Estimating GMM mixing coefficients typically involves either the expectation-maximization (EM) algorithm before VAE training or a multilayer perceptron (MLP), both of which increase computational complexity. To address these limitations, we propose the Deep ViT-VAE-GMM (DVVG) framework. First, we employ Jensen’s inequality to simplify the KL divergence computation, reducing gradient instability and improving training stability. Second, we introduce convolutional neural networks (CNNs) to adaptively estimate the mixing coefficients, enabling an end-to-end architecture while significantly lowering computational costs. Experimental results on benchmark datasets demonstrate that DVVG not only enhances VAE performance but also improves efficiency in modeling complex latent distributions. Our method effectively balances performance and computational feasibility, making it a practical solution for real-world image forgery localization. Full article

Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids. Full article

The authenticity of digital video content has become a critical issue in multimedia security due to the significant rise in video editing and manipulation in recent years. The detection of interframe forgeries is essential for identifying manipulations, including frame duplication, deletion, and insertion. These are popular techniques for altering video footage without leaving visible visual evidence. This study provides a detailed review of various methods for detecting video forgery, with a primary focus on interframe forgery techniques. The article evaluates approaches by assessing key performance measures. According to a statistical overview, machine learning has traditionally been used more frequently, but deep learning techniques are gaining popularity due to their outstanding performance in handling complex tasks and robust post-processing capabilities. The study highlights the significance of interframe forgery detection for forensic analysis, surveillance, and content moderation, as demonstrated through both evaluation and case studies. It aims to summarize existing studies and identify limitations to guide future research towards more robust, scalable, and generalizable methods, such as the development of benchmark datasets that reflect real-world video manipulation diversity. This emphasizes the necessity of creating large public datasets of manipulated high-resolution videos to support reliable integrity evaluations in dealing with widespread media manipulation. Full article

The rapid advancements in generative artificial intelligence (AI), particularly through models like Generative Adversarial Networks (GANs) and diffusion-based architectures, have made it increasingly difficult to distinguish between real and synthetically generated images. While these technologies offer benefits in creative domains, they also pose serious risks in terms of misinformation, digital forgery, and identity manipulation. This paper presents a novel hybrid deep learning model for detecting AI-generated images by integrating the ResNet-50 architecture with Squeeze-and-Excitation (SE) attention blocks. The proposed SE-ResNet50 model enhances channel-wise feature recalibration and interpretability by integrating Squeeze-and-Excitation (SE) blocks into the ResNet-50 backbone, enabling dynamic emphasis on subtle generative artifacts such as unnatural textures and semantic inconsistencies, thereby improving classification fidelity. Experimental evaluation on the CIFAKE dataset demonstrates the model’s effectiveness, achieving a test accuracy of 96.12%, precision of 97.04%, recall of 88.94%, F1-score of 92.82%, and an AUC score of 0.9862. The model shows strong generalization, minimal overfitting, and superior performance compared with transformer-based models and standard architectures like ResNet-50, VGGNet, and DenseNet. These results confirm the hybrid model’s suitability for real-time and resource-constrained applications in media forensics, content authentication, and ethical AI governance. Full article