Search Results (210)

To address the challenges of copyright protection for marine environmental datasets in open sharing environments, this paper proposes ST-Mark, a robust watermarking framework that synergistically integrates established geometric invariants and Quantization Index Modulation (QIM) techniques to meet the strict physical constraints of marine environmental datasets. The proposed method first extracts feature points by analyzing the spatiotemporal distribution of the data. It then constructs a local reference frame from the convex hull vertices and computes the geometrically invariant angles and distance ratios of the feature points relative to this reference pair to achieve robust partitioning. Finally, the watermark is embedded into the attribute domain of the grouped data through the Quantization Index Modulation (QIM) strategy while constraining perturbations within observational uncertainty bounds. Extensive experiments demonstrate that ST-Mark exhibits strong robustness: under extreme conditions such as temporal deletion attacks with an intensity of 0.9, the average normalized correlation (NC) remains above the robustness threshold of 0.75, with peak values reaching 0.99 on high-resolution datasets, although performance may fluctuate or fall near this threshold under severe spatial restrictions and numerical quantization, while still supporting reliable copyright verification under typical operational conditions. Full article

In the contemporary digital era, image watermarking is essential for protecting intellectual property due to the widespread unauthorized distribution of digital content. In this work, a robust and efficient image watermarking scheme for copyright protection is proposed. The method integrates wavelet packet decomposition (WPD) with an ensemble of bagged tree classifiers, forming the BT-WPD framework. In the proposed approach, wavelet packet coefficients extracted from each color channel are reorganized into structured batches that capture spatial frequency characteristics, enabling effective watermark embedding in the WPD domain guided by the bagged tree ensemble model. Experimental results demonstrate that the proposed method achieves high imperceptibility, with a peak signal-to-noise ratio (PSNR) exceeding 60 dB, while maintaining strong robustness against various image processing attacks. The method also exhibits low computational complexity during watermark extraction, making it suitable for practical applications. Furthermore, the framework is extended to support Quick Response (QR) code watermark embedding, demonstrating enhanced robustness and versatility for copyright protection in digital media systems. Full article

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability. Full article

Robust watermarking has emerged as a pivotal field in digital security, particularly for protecting intellectual property. This study introduces a robust watermarking framework specifically engineered to withstand JPEG compression attacks. To evaluate our scheme, several performance metrics should be considered, including robustness against intentional JPEG compression, embedding capacity, and the perceptual quality of watermarked images. For practical applications, we choose the quick response code to serve as the watermark, and we extend our evaluation to high-resolution pictures, both captured by the authors and obtained on the Internet with no copyright issues. These images possess resolutions that are dozens-of-times higher than conventional test images, facilitating a vastly increased capacity. This expanded capacity makes the integration of visual secret sharing practical, adding an additional layer of cryptographic protection to the watermarking scheme. Simulation results present the effectiveness of our implementation, demonstrating that our approach achieves a superior balance between data integrity and visual fidelity, even when subjected to rigorous attack conditions. Full article

In a digital watermarking technology system, robustness, imperceptibility and payload capacity are the three core performance indicators that restrict one another. How to achieve the optimal balance between the three is still the key scientific challenge to be solved in this field. This paper proposes a digital watermarking algorithm based on the magnitude coefficient of Non-Subsampled Shearlet Transform Fast and Accurate Polar Harmonic Fourier Moments (NSST-FAPHFMs) and the Weibull–Power Cauchy (W-PC) statistical model. The algorithm consists of two stages: watermark embedding and detection. In the embedding phase, the original image is first decomposed by NSST multi-scale decomposition, and the high-frequency subbands are divided into non-overlapping blocks and partitioned. High-energy coefficient blocks are extracted to obtain NSST-FAPHFM magnitude coefficient features, which serve as robust carriers for watermark embedding. In the detection phase, the W-PC distribution is used to accurately statistically model the above magnitude coefficients to characterize their heavy-tailed characteristics and strong correlation structure. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, and a blind watermark detection mechanism is further constructed by integrating the W-PC model with the Local Optimal Detector (LOD) under the Neyman–Pearson (N-P) criterion. Experimental results show that the proposed algorithm has good imperceptibility, and the area under the receiver operating characteristic curve (AUROC) can reach 0.9991 without attack. The algorithm maintains strong robustness against various attacks and can effectively realize the joint optimization of the three core performance indicators of the watermarking system. Full article

Digital watermarking is widely used to protect medical images in terms of ownership, authenticity, and traceability; however, the embedding process may introduce subtle modifications that can affect the reliability of deep-learning-based clinical analysis. Existing studies have shown that watermarking has a negligible effect on medical image classification; nevertheless, its impact on segmentation performance remains insufficiently explored. Therefore, this paper aims to investigate the effects of segmentation model enhancement on watermarked medical image analysis. In this context, three representative watermarking approaches were employed, and five baseline segmentation models, namely U-Net, ResUNet++, SegNet, FCDenseNet, and TernausNet, were evaluated on two benchmark datasets: LIDC-IDRI and BRISC. Additionally, a novel deep learning model with nested attention mechanisms was specifically designed to improve feature extraction and increase sensitivity to subtle pixel-level variations in watermarked images. Segmentation performance was assessed using five standard evaluation metrics, including mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and the 95th percentile Hausdorff Distance (HD95). The experimental results indicate consistently minor performance degradation across both datasets. For the BRISC dataset, the reduction in mIoU ranges from 0.15% to 0.44%, while for the LIDC-IDRI dataset, it ranges from 0.19% to 0.29% compared with the no-watermarking baseline. These findings provide quantitative insight into the compatibility of watermarking techniques for medical image protection with AI-based medical image segmentation systems, highlighting their potential for broader clinical application. Full article

Digital watermarking secures electroencephalogram (EEG) data in distributed Brain–Computer Interface (BCI) environments. However, existing single-domain deep learning schemes struggle to maintain robustness against clinical signal processing attacks due to EEG’s joint time–frequency nature. We introduce the Dual-Stream Frequency–Time Attention Network (DS-FTAN), utilizing an adaptive Spectral Gating Mechanism to embed information within robust, high-energy EEG spectral regions. A robustness simulation layer—encompassing resampling, spectral dropout, and band-pass filtering—is incorporated during training. Validations confirm DS-FTAN balances imperceptibility (PSNR > 36 dB) with reliable recovery. Specifically, it achieves >99.99% accuracy under no-attack conditions and maintains 86.52–98.77% accuracy across complex attacks (e.g., 50% cropping, band-pass filtering). This significantly outperforms time-domain baselines. Furthermore, DS-FTAN exhibits excellent zero-shot cross-channel generalization. It preserves diagnostic integrity, causing merely a 0.42% accuracy drop in downstream EEGNet intention recognition. Ultimately, this framework provides a reliable solution for privacy-preserving EEG data sharing. Full article

The widespread deployment of photovoltaic (PV) inverters with digital control and communication systems has increased the power grid’s attack surface, making it more vulnerable to cyberattacks. This creates a need for locally implementable attack-detection methods that do not disrupt inverter operation. This paper therefore proposes an irradiance-driven natural watermarking approach for decentralized detection of false data injection (FDI) attacks on inverter terminal measurements. The approach leverages irradiance-driven DC-link voltage variations to watermark the inverter outputs, generating a non-removable signature in the true measurements. The proposed method is evaluated using a real-time hardware-in-the-loop model of a three-phase grid-following PV inverter that captures PV-array and grid-connection dynamics. Implementation robustness is further assessed on a separate hardware grid-forming inverter testbed with non-idealized components. In the tested cases, the detection model identifies noise-injection and replay attacks within $15\mathrm{ms}$, while otherwise undetectable model-based attacks are revealed when DC-link voltage variations between 5% and 10% occur. These experimental results demonstrate that irradiance-driven natural watermarking can reveal FDI attacks without affecting normal inverter operation. Full article

To combat counterfeiting in down products and enhance enterprise credibility through technical means, this paper proposes a blockchain-based down quality traceability framework named DPT (down-product traceability). Built on Hyperledger Fabric, the framework integrates the InterPlanetary File System (IPFS), digital anti-counterfeiting watermarks (DW), QR codes, and category-specific encryption strategies to establish a trusted data chain throughout the entire supply chain. Role-based access control (RBAC) is adopted to ensure the secure submission and query of traceability information by all supply chain participants. A trinity data storage architecture is designed to achieve secure and efficient data management. A full-fledged application system was developed and deployed in cooperation with a leading down products enterprise to validate its practical applicability. Performance evaluation using Hyperledger Caliper 0.6.0, which focuses on throughput, latency, and resource utilization under stress testing, confirms that the DPT framework meets the requirements of real-world production. Furthermore, practical sales data verify that the proposed system effectively enhances consumer trust and mitigates counterfeiting behaviors in the market. Future work will focus on further optimizing write operation performance and evolving the system into a more robust clustered architecture. Full article

The integration of generative artificial intelligence (GAI) into digital learning environments is a profound socio-technical transformation. While GAI promises enhanced accessibility and efficiency, it simultaneously obscures the human creativity and intellectual labour that underpins digital knowledge production. This opacity limits creators’ visibility into how their work is used, evaluated, and monetised. This review application work investigates how several leading large language models, including ChatGPT (GPT-4o), Gemini (1.5 Flash), and DeepSeek (V3), interact with a creative platform hosting over 300 original essays, poems, and artworks from various human creatives. Our review reveals that despite clear evidence of models engaging with original materials, standard platform analytics of the average creative record no attribution, referrals, or traceable interaction from their end, rendering creators’ labour invisible. This compels critical examination of knowledge provenance and power within AI-mediated education. To address this, we propose a socio-technical framework, Chujoyi-TraceNet, not as a technical fix, but a mechanism to re-centre ethics, justice, and recognition in digital governance. By integrating real-time tracking, blockchain-enabled licensing, and metadata watermarking, Chujoyi-TraceNet operationalises the principles of equitable attribution. This study argues for a re-imagining of digital ecosystems in education, one that links the technical act of attribution to broader debates on digital labour, platform ethics, and the pursuit of social justice, thereby contributing to more democratic and accountable learning media in the era of Industry 4.0 and 5.0. Full article

Deepfakes and adjacent synthetic-media capabilities have become a systemic challenge for information integrity, security, and digital trust. Countermeasures now span passive detection methods that infer manipulation from content traces, active provenance systems that cryptographically bind metadata to media, and watermarking approaches that embed detectable signals into content or generative processes. This review presents a rigorous synthesis of tools and technologies to combat deepfakes across modalities (image, video, audio, and selected multimodal settings), drawing primarily from the peer-reviewed literature, standardized benchmarks, and official technical specifications and reports. The review analyzes detection methods, provenance and authentication technologies, with emphasis on cryptographic manifests and threat models, watermarking and content provenance, including diffusion-era watermarking and industrial deployments, adversarial robustness and attacker adaptation, datasets and benchmarks, evaluation metrics across tasks, and deployment and scalability constraints. A dedicated section addresses legal, ethical, and policy issues, focusing on emerging transparency obligations and platform governance. The review finds that no single countermeasure is sufficient in realistic adversarial settings. The strongest practical approach is a layered defense that combines provenance, watermarking, content-based detection, and human oversight. The study concludes with limitations of the current evidence base and prioritized research directions to improve generalization, interoperability, and trustworthy user experiences. Full article

The integrity protection of digital signals is an important task in modern applications. We propose DIPS (Data Integrity Protection of Signals), a fragile watermarking algorithm aiming to protect the integrity of sampled signals like images composed of pixels or sampled audio signals that can be divided into block units. The present paper starts with two works that propose fragile watermarking algorithms yielding high-quality watermarked objects, identifies their security vulnerabilities, and finally defines a method that embeds a compressed Message Authentication Code of each block into the LSBs of the block samples. As it modifies 2 bits per block at most, the introduced distortion is extremely low, thus resulting in a very high objective quality ($PSNR$). Experimental results confirming this characteristic are reported on real sampled signals such as speech, images, and ECG signals. Full article

The widespread distribution of digital videos in social networks, streaming services, and surveillance systems has increased the risk of manipulation, unauthorized redistribution, and adversarial tampering. This paper presents a lightweight deep learning framework for robust and imperceptible video watermarking designed specifically for cybersecurity environments. Unlike heavy architectures that rely on multi-scale feature extractors or complex adversarial networks, our model introduces a compact encoder–decoder pipeline optimized for real-time watermark embedding and recovery under adversarial attacks. The proposed system leverages spatial attention and temporal redundancy to ensure robustness against distortions such as compression, additive noise, and adversarial perturbations generated via Fast Gradient Sign Method (FGSM) or recompression attacks from generative models. Experimental simulations using a reduced Kinetics-600 subset demonstrate promising results, achieving an average PSNR of 38.9 dB, SSIM of 0.967, and Bit Error Rate (BER) below 3% even under FGSM attacks. These results suggest that the proposed lightweight framework achieves a favorable trade-off between resilience, imperceptibility, and computational efficiency, making it suitable for deployment in video forensics, authentication, and secure content distribution systems. Full article

At present, active defense strategies based on digital watermarking mainly rely on post-event watermark extraction, which verifies the occurrence of deepfake events by measuring the degree of watermark degradation, or on adversarial watermarks to interfere with image generation. To overcome these limitations, we propose a unified watermarking framework that can restore the original content of images tampered with by deepfakes. This scheme integrates three core components: an encoder for watermark pre-embedding, a decoder for robust watermark extraction, and a face restorer for watermark-guided image restoration. Numerous experiments have shown that this method has achieved good results in terms of extraction accuracy and recovery performance, thereby verifying the effectiveness of this approach. Full article

This study proposes a robust digital watermarking method combining the discrete wavelet transform and genetic algorithms (GAs) to enhance the protection of high-value digital assets against geometric attacks. By optimizing embedding strength through GA, this method achieves high imperceptibility and resilience under scaling, rotation, and translation. Experimental results demonstrate improved watermark recovery and visual fidelity, providing a practical solution for digital anti-counterfeiting. Full article