Search Results (288)

With the rapid advancement of speech synthesis and voice conversion technologies, audio deepfake techniques have posed serious threats to information security. Existing detection methods often lack robustness when confronted with environmental noise, signal compression, and ambiguous fake features, making it difficult to effectively identify highly concealed fake audio. To address this issue, this paper proposes a Dual-Path Time-Frequency Attention Network (DPTFAN) based on Pythagorean Hesitant Fuzzy Sets (PHFS), which dynamically characterizes the reliability and ambiguity of fake features through uncertainty modeling. It introduces a dual-path attention mechanism in both time and frequency domains to enhance feature representation and discriminative capability. Additionally, a Lightweight Fuzzy Branch Network (LFBN) is designed to achieve explicit enhancement of ambiguous features, improving performance while maintaining computational efficiency. On the ASVspoof 2019 LA dataset, the proposed method achieves an accuracy of 98.94%, and on the FoR (Fake or Real) dataset, it reaches an accuracy of 99.40%, significantly outperforming existing mainstream methods and demonstrating excellent detection performance and robustness. Full article

Reducing reliance on private vehicles, optimizing public spaces, and adopting low-carbon, energy-efficient practices are essential strategies for advancing sustainable urban development. This study investigates user perceptions and spatial experiences at Hakata Station in Fukuoka, Japan, by analyzing online reviews collected over 1 year. The results indicate that: (1) Using TF–IDF vectorization and K-means clustering (K = 5), five major semantic themes were identified, and a chi-square test (χ²(16) = 632.00, p < 0.001) confirmed their strong correspondence with the station’s five functional zones. This revealed a cognitive mapping effect between users’ semantic structures and spatial functions. (2) Six environmental psychology indicators—Wayfinding Usability, Crowding Density, Seating and Rest Availability, Functional Convenience, Environmental Quality, and Information Legibility—were established. Logistic regression showed that only Functional Convenience significantly predicted positive sentiment (OR = 31.6, p = 0.05), underscoring the emotional influence of smooth circulation and well-integrated commercial facilities. (3) Process-intensive areas exhibited emotional accumulation and cognitive strain, while restorative zones reduced mental fatigue; moderate spatial concealment enhanced exploration, and a shared social atmosphere fostered belongingness. The findings elucidate the psychological correspondence between semantic structures and spatial functions, providing user-centered indicators for urban node design that promote comfort, accessibility, and urban sustainability. Full article

Against the backdrop of China’s “East-West Computing Resource Transfer” and “Digital-Real Integration” national strategies, computing power has emerged as a core engine driving the digital economy. However, existing research lacks in-depth exploration of the micro-level mechanisms through which computing power operates as a strategic digital resource at the firm level and transforms into competitive advantages. This study examines a sample of manufacturing firms listed on China’s A-share markets from 2011 to 2022, treating the establishment of intelligent computing centers by firms as a quasi-natural experiment. Employing a staggered difference-in-differences model combined with causal inference strategies such as double machine learning, we empirically test the impact of computing power investment on firms’ new quality productivity. The findings reveal that computing power investment significantly enhances new quality productivity, primarily through enabling dynamic capabilities: it strengthens risk perception capabilities by improving information environments, enabling intelligent risk monitoring, and enhancing decision-making resilience; it elevates innovation opportunity-capturing capabilities by expanding the scope of innovation search, accelerating innovation iteration, and facilitating cross-domain knowledge integration; and it achieves data element reconstruction through constructing data infrastructure capabilities, improving data operational efficiency, and optimizing data ecosystem collaboration. Further analysis demonstrates that this promotional effect is more pronounced in firms with strong executive digital cognition and intense market competition, and is more significant among non-heavily polluting, high-tech firms with high absorptive capacity, those located in eastern regions, and those with superior digital endowments. Extended analysis also reveals that the new quality productivity gains from computing power investment drive optimal allocation of human capital while potentially inducing strategic information concealment behaviors as firms seek to protect competitive advantages. By conceptualizing computing power as a contestable strategic resource at the micro level, this study unveils the micro-mechanisms of digital transformation through a dynamic capability framework, offering important implications for firms and governments in optimizing digital strategies. Full article

Human immunodeficiency virus type 1 (HIV-1) is known to activate cytosolic DNA sensor pathways, such as the cGAS-STING pathway, the activation of which leads to interferon production. The primary source of this activation is reverse transcription of the viral RNA genome into cDNA, which occurs in the cytoplasm. However, the degree of cytosolic DNA sensor activation during HIV-1 infection is significantly lower compared to that induced by DNA-containing viruses or even by the related HIV-2. This can be attributed to the successful evasion of innate immune recognition pathways by HIV-1, particularly through the disruption of cGAS-STING signaling pathway activation. In this review, we summarize the available information regarding the mechanisms employed by HIV-1 to conceal viral cDNA from cytosolic DNA sensors. Deciphering these mechanisms may reveal potential vulnerabilities that could be targeted to develop novel antiviral approaches. Full article

Traditional image watermarking technology focuses on the robustness and imperceptibility of the copyright information embedded in the cover image. However, in addition to copyright theft, the cover images stored and transmitted in the open network environment is facing the threat of being identified and retrieved by deep neural network (DNN) with malicious purpose, which is a new privacy threat. Therefore, it is essential to protect the copyright and the privacy of cover image simultaneously. In this paper, a novel cover-concealed image watermarking (CCIW) is proposed, which combines conditional generative adversarial networks with channel attention mechanisms to generate adversarial examples of the cover image containing invisible copyright information. This method can effectively prevent privacy leakage and copyright infringement simultaneously, since the cover image cannot be collected and processed by DNNs without permission, and the embedded copyright information is hardly to be removed. The experimental results show that the proposed method achieved a success rate of adversarial attack over 98% on the Caltech256 dataset, and the generated adversarial examples have good image quality. The accuracy of copyright information extraction is close to 100%, and it also exhibits good robustness in different noise environments. Full article

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate Wavelet Transforms with various deep learning architectures, namely Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), and Graph Convolutional Networks (GCNs). We conducted a comprehensive set of experiments on the FashionMNIST and MNIST datasets to evaluate our framework’s performance against several adversarial attacks. The results demonstrate that our multi-stage approach significantly enhances resilience. Notably, while CNN architectures provide the highest baseline accuracy, BNNs exhibit superior intrinsic robustness against gradient-based attacks. For instance, under the Fast Gradient Sign Method (FGSM) attack on the MNIST dataset, our BNN-based models maintained an accuracy of over 98%, whereas the performance of comparable CNN models dropped sharply to between 10% and 18%. This research provides a robust and effective method for developing next-generation secure steganography systems. Full article

Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts. Full article

This study explores the role of social trust, a critical informal institution, in mitigating corporate brownwashing—the strategic concealment of positive environmental performance. Drawing on a panel of 15,081 firm-year observations from Chinese A-share listed firms between 2010 and 2022, we operationalize brownwashing as a strategy where firms demonstrate substantive environmental compliance (i.e., no environmental penalties) while simultaneously practicing symbolic verbal conservatism (below-median environmental disclosure). Our empirical analysis reveals that higher regional social trust significantly curbs the propensity for firms to engage in brownwashing. This effect is not only statistically significant but also economically meaningful: a one-standard-deviation increase in social trust is associated with a 1.85 percentage point decrease in the likelihood of brownwashing. This effect operates through two key channels: enhancing stakeholder monitoring and reinforcing internal governance for environmental accountability. The impact of trust is significantly amplified under specific conditions: its role is more pronounced where formal sustainability regulations are weaker, highlighting trust as a crucial informal pillar of the sustainability governance architecture, and its inhibitory effect is strengthened when firms face higher reputational risks tied to their environmental performance. This study makes several contributions: it provides broad, cross-industry evidence on a key challenge in sustainability reporting; offers empirical support for the “trust fidelity” theory in the context of environmental disclosure; and develops a ‘channel-amplifier’ framework that advances our understanding of the complex institutional interplay required to foster corporate environmental transparency. Full article

With the rapid advancement of hyperspectral remote sensing technology, the security of hyperspectral images (HSIs) has become a critical concern. However, traditional image encryption methods—designed primarily for grayscale or RGB images—fail to address the high dimensionality, large data volume, and spectral-domain characteristics inherent to HSIs. Existing chaotic encryption schemes often suffer from limited chaotic performance, narrow parameter ranges, and inadequate spectral protection, leaving HSIs vulnerable to spectral feature extraction and statistical attacks. To overcome these limitations, this paper proposes a novel hyperspectral image encryption algorithm based on a newly designed two-dimensional cross-coupled hyperchaotic map (2D-CSCM), which synergistically integrates Cubic, Sinusoidal, and Chebyshev maps. The 2D-CSCM exhibits superior hyperchaotic behavior, including a wider hyperchaotic parameter range, enhanced randomness, and higher complexity, as validated by Lyapunov exponents, sample entropy, and NIST tests. Building on this, a layered encryption framework is introduced: spectral-band scrambling to conceal spectral curves while preserving spatial structure, spatial pixel permutation to disrupt correlation, and a bit-level diffusion mechanism based on dynamic DNA encoding, specifically designed to secure high bit-depth digital number (DN) values (typically >8 bits). Experimental results on multiple HSI datasets demonstrate that the proposed algorithm achieves near-ideal information entropy (up to 15.8107 for 16-bit data), negligible adjacent-pixel correlation (below 0.01), and strong resistance to statistical, cropping, and differential attacks (NPCR ≈ 99.998%, UACI ≈ 33.30%). The algorithm not only ensures comprehensive encryption of both spectral and spatial information but also supports lossless decryption, offering a robust and practical solution for secure storage and transmission of hyperspectral remote sensing imagery. Full article

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels. Full article

Human action recognition (HAR) based on WiFi channel state information (CSI) has attracted growing attention due to its contactless, privacy-preserving, and cost-effective nature. Recent studies have reported promising results by leveraging deep learning and image-based representations of CSI. However, methodological flaws in experimental protocols, particularly improper dataset partitioning, can lead to data leakage and significantly overestimate model performance. In this paper, we critically analyze a recently published WiFi-CSI-based HAR approach that converts CSI measurements into images and applies deep learning for classification. We show that the original evaluation relied on random data splitting without subject separation, causing substantial data leakage and inflated results. To address this, we reimplemented the method using subject-independent partitioning, which provides a realistic assessment of generalization ability. Furthermore, we conduct a quantitative study of post-training quantization under both correct and flawed partitioning strategies, revealing that methodological errors can conceal the true performance degradation of compressed models. Our findings demonstrate that evaluation protocols strongly influence reported outcomes, not only for baseline models but also for engineering decisions regarding model optimization and deployment. Based on these insights, we provide guidelines for designing robust experimental protocols in WiFi-CSI-based HAR to ensure methodological integrity and reproducibility. Full article

Crossfire attacks disrupt network services by targeting critical links of server groups, causing traffic congestion and server failures that prevent legitimate users from accessing services. To counter this threat, this study proposes a novel topology spoofing defense mechanism based on a sequence-based Graph Neural Network–Moving Target Defense (ENRNN-MTD). During the reconnaissance phase, the method employs a GNN to generate multiple random and diverse virtual topologies, which are mapped to various external hosts. This obscures the real internal network structure and complicates the attacker’s ability to accurately identify it. In the attack phase, an IP random-hopping mechanism using a chaotic sequence is introduced to conceal node information and increase the cost of launching attacks, thereby enhancing the protection of critical services. Experimental results demonstrate that, compared to existing defense mechanisms, the proposed approach exhibits significant advantages in terms of deception topology randomness, defensive effectiveness, and system load management. Full article

Background and Objectives: Clinical ethical judgments are often elicited through scenario-based (vignette-based) dilemmas that guide interpretation, reasoning, and moral judgment. Despite its importance, little is known about how healthcare professionals and students respond to such scenario-based dilemmas in Eastern European settings. This study explored differences in ethical decision-making between senior medical/dental students and practicing clinicians in Romania, focusing on how scenarios-based dilemmas influence conditional versus categorical responses. Materials and Methods: A cross-sectional survey was conducted with 244 participants (51 senior students; 193 practitioners). Respondents completed a validated 35-item questionnaire presenting hypothetical ethical scenarios across seven domains: informed consent, confidentiality, medical errors, public health duties, end-of-life decisions, professional boundaries, and crisis ethics. Each scenario used a Yes/No/It depends response structure. Group comparisons were analyzed using chi-square and non-parametric tests (α = 0.05). Results: Scenario-based dilemmas elicited frequent conditional reasoning, with “It depends” emerging as the most common response (47.8%). Strong consensus appeared in rejecting concealment of harmful errors and in treating unvaccinated families, reflecting robust professional norms. Divergences arose in areas where scenario-based dilemmas emphasized system-level duties: students more often supported annual influenza vaccination (52.9% vs. 32.6%, p = 0.028) and organ purchase authorization (76.47% vs. 62. 18%, p = 0.043), while practitioners more frequently endorsed higher insurance contributions for unhealthy lifestyles (48.7% vs. 23.5%, p = 0.003). Conclusions: Scenario-based dilemmas strongly shape moral decision-making in healthcare. While students tended toward principle-driven transparency, practitioners showed pragmatic orientations linked to experience and system stewardship. To promote high-quality clinical work and align decision-making with best practice and health policy, our findings support institutional protocols for transparent error disclosure, continuing professional development in ethical communication, the possible adoption of annual influenza vaccination policies for healthcare personnel as policy options rather than categorical imperatives, and structured triage frameworks during crisis situations. These proposals highlight how scenario-based ethics training can strengthen both individual reasoning and systemic resilience. Full article

Audio steganography exploits redundancies in the human auditory system to conceal secret information within cover audio, ensuring that the hidden data remains undetectable during normal listening. However, recent research shows that current audio steganography techniques are vulnerable to detection by deep learning-based steganalyzers, which analyze the high-dimensional features of stego audio for classification. While deep learning-based steganography has been extensively studied for image covers, its application to audio remains underexplored, particularly in achieving robust embedding and extraction with minimal perceptual distortion. We propose a diffusion-based audio steganography model comprising two primary modules: (i) a diffusion-based embedding module that autonomously integrates secret messages into cover audio while preserving high perceptual quality and (ii) a corresponding diffusion-based extraction module that accurately recovers the embedded data. The framework supports both pre-existing cover audio and the generation of high-quality steganographic cover audio with superior perceptual quality for message embedding. After training, the model achieves state-of-the-art performance in terms of embedding capacity and resistance to detection by deep learning steganalyzers. The experimental results demonstrate that our diffusion-based approach significantly outperforms existing methods across varying embedding rates, yielding stego audio with superior auditory quality and lower detectability. Full article

Purpose: Luggage X-rays suffer from low contrast, material overlap, and noise; dual-energy imaging reduces ambiguity but creates colour biases that impair segmentation. This study aimed to (1) employ connotative fusion by embedding realistic detonator patches into real X-rays to simulate threats and enhance unattended detection without requiring ground-truth labels; (2) thoroughly evaluate fusion techniques in terms of balancing image quality, information content, contrast, and the preservation of meaningful features. Methods: A total of 1000 X-ray luggage images and 150 detonator images were used for fusion experiments based on deep learning, transform-based, and feature-driven methods. The proposed approach does not need ground truth supervision. Deep learning fusion techniques, including VGG, FusionNet, and AttentionFuse, enable the dynamic selection and combination of features from multiple input images. The transform-based fusion methods convert input images into different domains using mathematical transforms to enhance fine structures. The Nonsubsampled Contourlet Transform (NSCT), Curvelet Transform, and Laplacian Pyramid (LP) are employed. Feature-driven image fusion methods combine meaningful representations for easier interpretation. Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Random Forest (RF), and Local Binary Pattern (LBP) are used to capture and compare texture details across source images. Entropy (EN), Standard Deviation (SD), and Average Gradient (AG) assess factors such as spatial resolution, contrast preservation, and information retention and are used to evaluate the performance of the analysed methods. Results: The results highlight the strengths and limitations of the evaluated techniques, demonstrating their effectiveness in producing sharpened fused X-ray images with clearly emphasized targets and enhanced structural details. Conclusions: The Laplacian Pyramid fusion method emerges as the most versatile choice for applications demanding a balanced trade-off. This is evidenced by its overall multi-criteria balance, supported by a composite (geometric mean) score on normalised metrics. It consistently achieves high performance across all evaluated metrics, making it reliable for detecting concealed threats under diverse imaging conditions. Full article