Search Results (101)

Deepfake technology, driven by advances in artificial intelligence (AI) and deep learning (DL), has become one of the foremost threats to digital trust and the authenticity of information. Despite the rapid development of deepfake detection methods, the dynamic evolution of generative models continues to outpace current mitigation efforts. This highlights the pressing need for more effective and proactive deepfake prevention strategy. This study introduces a comprehensive and multifaceted deepfake prevention framework that leverages both technical and non-technical countermeasures and involves collaboration among key stakeholders in a unified structure. The proposed framework has four modules: trusted content assurance, detection and monitoring, awareness and human-in-the-loop verification, and policy, governance, and regulation. The framework uses a combination of hybrid watermarking and embedding techniques, as well as cryptographic digital signature algorithms (DSAs) and blockchain technologies, to make sure that the media is authentic, traceable, and cannot be denied. Comparative experiments were conducted in this research using both classical and post-quantum DSAs to evaluate their efficiency, resource consumption, and gas costs in blockchain operations. The results revealed that the Falcon-512 algorithm outperformed other post-quantum algorithms while consuming fewer resources and lowering gas costs, making it a preferable option for real-time, quantum-resilient deepfake prevention. The framework also employed AI-based detection models and human oversight to enhance detection accuracy and robustness. Overall, this research offers a novel, multifaceted, and governance-aware strategy for deepfake prevention. The proposed approach significantly contributes to mitigating deepfake threats and offers a practical foundation for secure and transparent digital media ecosystems. Full article

Audio watermarking has been introduced to give authors and owners control over the use of audio signals. The need for control has increased with advances in digital communications. Due to the diversity of applications and trade-offs in performance, different works on audio watermarking have been reported, and new algorithms have been proposed. Being an active area of research, an updated background on the reported algorithms helps designers understand the trends and tools used in published works. The recent emergence of innovative methods to enhance performance, the need for new requirements due to increased potential attacks, advancements in technology with a tendency toward improved audio quality, and the development of new applications have made it challenging to fit these developments into existing reviews and classifications. This paper fills this gap, presenting a review, analysis, and classification of the most recent conventional watermarking algorithms from 2016 to the present. Our study reveals the predominance of blind watermarking approaches and the widespread adoption of wavelet-domain techniques due to their favorable balance between robustness and imperceptibility. We proposed organizing, discussing, and comparing the methods based on performance criteria, imperceptibility, capacity, security, computational complexity, and robustness, and setting thresholds to categorize them. Additionally, a novel systematization based on the processes involved in the various stages of watermarking is presented. The purpose is to make it easier to identify the performance criteria that could be useful and important for different applications. Full article

Illegal broadcasting is one of the primary challenges for Over the Top (OTT) service providers. Watermarking is a method used to trace illegal redistribution of video content. However, watermarking introduces processing overhead due to the embedding of unique patterns into the video content, which results in additional latency. End-to-end network latency, caused by network congestion or heavy load on the origin server, can slow data transmission, impacting the time it takes for the segment to reach the client. This paper addresses 5xx errors (e.g., 503, 504) at the Content Delivery Network (CDN) in real-world video streaming platforms, which can negatively impact Quality of Experience (QoE), particularly when watermarking techniques are employed. To address the performance issues caused by the integration of watermarking technology, we enhanced the system architecture by introducing and optimizing a shield cache in front of the packager at the origin server and fine-tuning the CDN configuration. These optimizations significantly reduced the processing load on the packager, minimized latency, and improved overall content delivery. As a result, we achieved a 6% improvement in the Key Performance Indicator (KPI), reflecting enhanced system stability and video quality. Full article

With the widespread application of 3D models derived from oblique photography, the need for copyright protection and trusted transactions has risen significantly. Traditional transactions often depend on third parties, making it difficult to balance copyright protection with transaction credibility and to safeguard the rights and interests of both parties. To address these challenges, this paper proposes a novel trusted-transaction scheme that integrates smart contracts with zero-watermarking technology. Firstly, the skewness of the oblique-photography 3D model data is employed to construct a zero-watermark identifier, which is stored in the InterPlanetary File System (IPFS) alongside encrypted data for trading. Secondly, smart contracts are designed and deployed. Lightweight information, such as IPFS data addresses, is uploaded to the blockchain by invoking these contracts, and transactions are conducted accordingly. Finally, the blockchain system automatically records the transaction process and results on-chain, providing verifiable transaction evidence. The experimental results show that the proposed zero-watermarking algorithm resists common attacks. The trusted-transaction framework not only ensures the traceability and trustworthiness of the entire transaction process but also safeguards the rights of both parties. This approach effectively protects copyright while ensuring the reliability of the transactions. Full article

Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of soil moisture variables over extended horizons. This study proposes a hybrid soil moisture prediction method, integrating a long short-term memory (LSTM) network and extreme gradient boosting (XGBoost) model for multistep soil moisture prediction at 24 h, 72 h, and 168 h horizons. The LSTM captures temporal dependencies and extracts high-level features from the dataset, which are then used by XGBoost for final predictions. The study uses real-world data from the D.A.T.A (Demonstrating Applied Technology in Agriculture) research farm at ABAC (Abraham Baldwin Agricultural College) Tifton, GA, USA, utilizing watermark soil moisture sensors and weather station’s data installed on the farm. Results show that the proposed method outperforms other hybrid models, achieving R² values of 98.67%, 98.54%, and 98.56% for 24, 72, and 168 h predictions, respectively. The study findings highlight that LSTM-XGBoost offers a precise long-term soil moisture prediction, making it a practical tool for real-time irrigation scheduling, enhancing water use efficiency in precision agriculture. Full article

In this paper, a security-enhanced three-dimensional (3D) image hiding and encryption method is proposed by combining a layer-based phase-only hologram (POH) under structured light illumination with chaotic encryption and digital image watermarking technology. In the proposed method, the original 3D plaintext image is firstly encoded into a layer-based POH and then further encrypted into an encrypted phase with the help of a chaotic random phase mask (CRPM). Subsequently, the encrypted phase is embedded into a visible ciphertext image by using a digital image watermarking technology based on discrete wavelet transform (DWT) and singular value decomposition (SVD), leading to a 3D image hiding with high security and concealment. The encoding of POH and the utilization of CRPM can substantially enhance the level of security, and the DWT-SVD-based digital image watermarking can effectively hide the information of the 3D plaintext image in a visible ciphertext image, thus improving the imperceptibility of valid information. It is worth noting that the adopted structured light during the POH encoding possesses many optical parameters, which are all served as the supplementary keys, bringing about a great expansion of key space; meanwhile, the sensitivities of the wavelength key and singular matrix keys are also substantially enhanced thanks to the introduction of structured light, contributing to a significant enhancement of security. Numerical simulations are performed to demonstrate the feasibility of the proposed 3D image hiding method, and the simulation results show that the proposed method exhibits high feasibility and apparent security-enhanced effect as well as strong robustness. Full article

Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications. Full article

Symmetries and symmetry-breaking play significant roles in data security. Digital watermarking is widely employed in information security fields such as copyright protection and traceability. With the continuous advancement of technology, the research into and application of digital watermarking face numerous challenges. To gain a comprehensive understanding of the current research status and trends in the development of digital watermarking, this paper conducts a bibliometric analysis using the CiteSpace software, focusing on 8621 publications related to digital watermarking (watermark/watermarking) from the Web of Science (WOS) Core Collection database, spanning from 2004 to 2024. This study explores the research landscape and future trends in digital watermarking from various perspectives, including annual publication volume, keyword co-occurrence and burst detection, leading authors, research institutions, and publishing countries or regions. The results reveal a regional concentration of research efforts, with early research being primarily dominated by the United States, Taiwan, and South Korea, while recent years have seen a rapid rise in research from China and India. However, global academic collaboration remains relatively fragmented and lacks a well-integrated international research network. Keyword analysis indicates that research hotspots have expanded from traditional copyright protection to data integrity verification, multimedia watermarking, and the incorporation of intelligent technologies. Notably, the introduction of deep learning has propelled watermarking algorithms toward greater sophistication and intelligence. Using CiteSpace, this study is the first to systematically illustrate the dynamic evolution of digital watermarking research over the past 20 years, focusing on thematic trends and regional distributions. Unlike previous reviews that rely mainly on qualitative analyses, this study offers a quantitative and visualized perspective. These findings provide concrete references for the future development of more targeted research efforts. Full article

In the field of 3D technology, depth-image-based rendering (DIBR) has been widely adopted due to its inherent advantages including low data volume and strong compatibility. However, during network transmission of DIBR 3D images, both center and virtual views are susceptible to unauthorized copying and distribution. To protect the copyright of these images, this paper proposes a channel interaction mamba-guided generative adversarial network (CIMGAN) for DIBR 3D image watermarking. To capture cross-modal feature dependencies, a channel interaction mamba (CIM) is designed. This module enables lightweight cross-modal channel interaction through a channel exchange mechanism and leverages mamba for global modeling of RGB and depth information. In addition, a feature fusion module (FFM) is devised to extract complementary information from cross-modal features and eliminate redundant information, ultimately generating high-quality 3D image features. These features are used to generate an attention map, enhancing watermark invisibility and identifying robust embedding regions. Compared to the current state-of-the-art (SOTA) 3D image watermarking methods, the proposed watermark model shows superior performance in terms of robustness and invisibility while maintaining computational efficiency. Full article

Plastic packaging materials can play a significant role in turning the plastic industry towards a circular economy, owing to their large volumes and short product lifetimes. This study emphasizes the role and interaction of design for recycling (DfR), appropriate specifications, and efficient sorting. DfR is enhancing the recyclability of plastic packaging by selecting appropriate materials and designs, improving the quality of recyclates without compromising safety or the original requirement. A significant barrier to achieving a circular economy is the lack of comprehensive standards for recycled plastics. While some specifications exist, a more integrated and globally accepted standardization regime, similar to that in the aerospace industry, is necessary to ensure quality and consistency in recycled materials. The potential of advanced sorting technologies to improve sorting efficiency and feedstock quality is highlighted, significantly enhancing recovery yields and the quality of recyclates. Information-based tracking technologies, such as digital watermarks, offer substantial benefits in identifying and sorting materials with high granularity, improving sorting mechanisms, enhancing resource recovery, and providing valuable data for stakeholders across the plastic value chain. The implementation of information-based technologies can reduce production costs and environmental impacts, with exemplary calculations indicating a potential 30% reduction in the production cost of PP recyclate. Full article

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments. Full article

Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value. Full article

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedded watermarks are generated, enabling an assessment of how effectively the sensor and its signal-conditioning circuitry handle these modified signals. A Variational Autoencoder (VAE) framework is employed to generate the watermarked ECG signals, addressing critical concerns in the digital era, such as data security, authenticity, and copyright protection. Three watermarking strategies are examined in this study: embedding watermarks in the mean (μ) of the VAE’s latent space, embedding them through the latent variable (z), and using post-reconstruction watermarking in the frequency domain. Experimental results demonstrate that watermarking applied through the mean (μ) and in the frequency domain achieves a low Mean Squared Error (MSE) while maintaining stable signal fidelity across varying watermark strengths (α), latent space dimensions, and noise levels. These findings indicate that the mean (μ) and frequency domain methods offer robust performance and are minimally affected by changes in these parameters, making them particularly suitable for preserving ECG signal quality. By contrasting these methods, this study provides insights into selecting the most appropriate watermarking technique for ECG sensor applications. Incorporating watermarking into sensor design not only strengthens data security and authenticity but also supports reliable signal acquisition in modern healthcare environments. Overall, the results underscore the effectiveness of combining VAEs with watermarking strategies to produce high-fidelity, resilient ECG signals for both sensor performance evaluation and the protection of digital content. Full article

Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design. Full article

This paper introduces PK-Judge, a novel neural network watermarking framework designed to enhance the intellectual property (IP) protection by incorporating an asymmetric cryptograp hic approach in the verification process. Inspired by the paradigm shift from HTTP to HTTPS in enhancing web security, this work integrates public key infrastructure (PKI) principles to establish a secure and verifiable watermarking system. Unlike symmetric approaches, PK-Judge employs a public key infrastructure (PKI) to decouple ownership validation from the extraction process, significantly increasing its resilience against adversarial attacks. Additionally, it incorporates a robust challenge-response mechanism to mitigate replay attacks and leverages error correction codes (ECC) to achieve an Effective Bit Error Rate (EBER) of zero, ensuring watermark integrity even under conditions such as fine-tuning, pruning, and overwriting. Furthermore, PK-Judge introduces a new requirement based on the principle of separation of privilege, setting a foundation for secure and scalable watermarking mechanisms in machine learning. By addressing these critical challenges, PK-Judge advances the state-of-the-art in neural network IP protection and integrity, paving the way for trust-based AI technologies that prioritize security and verifiability. Full article