Search Results (420)

This paper introduces a robust and imperceptible video watermarking framework designed for blind extraction in dynamic video environments. The proposed method operates in the spatial domain and combines multiscale perceptual analysis, adaptive Just Noticeable Difference (JND)-based quantization, and temporal redundancy via multiframe embedding. Watermark bits are embedded selectively in blocks with high perceptual masking using a QIM strategy, and the corresponding DCT coefficients are estimated directly from the spatial domain to reduce complexity. To enhance resilience, each bit is redundantly inserted across multiple keyframes selected based on scene transitions. Extensive simulations over 21 benchmark videos (CIF, 4CIF, HD) validate that the method achieves superior performance in robustness and perceptual quality, with an average Bit Error Rate (BER) of 1.03%, PSNR of 50.1 dB, SSIM of 0.996, and VMAF of 97.3 under compression, noise, cropping, and temporal desynchronization. The system outperforms several recent state-of-the-art techniques in both quality and speed, requiring no access to the original video during extraction. These results confirm the method’s viability for practical applications such as copyright protection and secure video streaming. Full article

Watermarking protocols represent a possible solution to the problem of digital copyright protection of content distributed on the Internet. Their implementations, however, continue to be a complex problem due to the difficulties researchers encounter in proposing secure, easy-to-use and, at the same time, “trusted third parties” (TTPs)-free solutions. In this regard, implementations based on blockchain and smart contracts are among the most advanced and promising, even if they are affected by problems regarding the performance and privacy of the information exchanged and processed by smart contracts and managed by blockchains. This paper presents a watermarking protocol implemented by smart contracts and blockchain. The protocol uses a “layer-2” blockchain execution model and performs the computation in “trusted execution environments” (TEEs). Therefore, its implementation can guarantee efficient and confidential execution without compromising ease of use or resorting to TTPs. The protocol and its implementation can, thus, be considered a valid answer to the “trilemma” that afflicts the use of blockchains, managing to guarantee decentralization, security, and scalability. 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

Large language models (LLMs) pose significant challenges to content authentication, as their sophisticated generation capabilities make distinguishing AI-produced text from human writing increasingly difficult. Current detection methods suffer from limited information capture, poor rate–distortion trade-offs, and vulnerability to adversarial perturbations. We present CurveMark, a novel dual-channel detection framework that combines probability curvature analysis with dynamic semantic watermarking, grounded in information-theoretic principles to maximize mutual information between text sources and observable features. To address the limitation of requiring prior knowledge of source models, we incorporate a Bayesian multi-hypothesis detection framework for statistical inference without prior assumptions. Our approach embeds imperceptible watermarks during generation via entropy-aware, semantically informed token selection and extracts complementary features from probability curvature patterns and watermark-specific metrics. Evaluation across multiple datasets and LLM architectures demonstrates 95.4% detection accuracy with minimal quality degradation (perplexity increase < 1.3), achieving 85–89% channel capacity utilization and robust performance under adversarial perturbations (72–94% information retention). Full article

The widespread deployment of large language models (LLMs) has raised urgent demands for verifiable content attribution and misuse mitigation. Existing text watermarking techniques often struggle in black-box or sampling-based scenarios due to limitations in robustness, imperceptibility, and detection generality. These challenges are particularly critical in open-access settings, where model internals and generation logits are unavailable for attribution. To address these limitations, we propose CWS (Contrastive Watermarking with Semantic Modeling)—a novel keyless watermarking framework that integrates contrastive semantic token selection and shared embedding space alignment. CWS enables context-aware, fluent watermark embedding while supporting robust detection via a dual-branch mechanism: a lightweight z-score statistical test for public verification and a GRU-based semantic decoder for black-box adversarial robustness. Experiments on GPT-2, OPT-1.3B, and LLaMA-7B over C4 and DBpedia datasets demonstrate that CWS achieves F1 scores up to 99.9% and maintains F1 ≥ 93% under semantic rewriting, token substitution, and lossy compression (ε ≤ 0.25, δ ≤ 0.2). The GRU-based detector offers a superior speed–accuracy trade-off (0.42 s/sample) over LSTM and Transformer baselines. These results highlight CWS as a lightweight, black-box-compatible, and semantically robust watermarking method suitable for practical content attribution across LLM architectures and decoding strategies. Furthermore, CWS maintains a symmetrical architecture between embedding and detection stages via shared semantic representations, ensuring structural consistency and robustness. This semantic symmetry helps preserve detection reliability across diverse decoding strategies and adversarial conditions. Full article

Remote sensing object detection (RSOD) models are widely deployed on edge devices for critical applications. Their security and integrity have become urgent concerns. This work proposes a fragile model watermarking method that enables black-box integrity verification for RSOD models. Specifically, for a given RSOD model, we construct class-specific sensitive object triggers and corresponding fragile watermark samples for each target category. During the trigger generation process, a trained surrogate model is first employed to construct the initial sensitive object trigger, where real objects are utilized to guide the trigger to acquire weak semantic features of the target class. This trigger is then jointly optimized using both the original model and a tampered version. The original model ensures that the trigger remains recognizable, while the tampered model encourages sensitivity to parameter changes. During integrity verification, the model is queried with all the fragile watermark samples. The model is considered intact only if all predictions match the expected results. Extensive experiments demonstrate that the proposed method is effective across multiple RSOD models. It exhibits high sensitivity to various model modifications, including backdoor injection, fine-tuning, pruning, random parameter perturbation, and model compression. Full article

Because neural networks pervade many industrial domains and are increasingly complex and accurate, the trained models themselves have become valuable intellectual properties. Developing highly accurate models demands increasingly higher investments of time, capital, and expertise. Many of these models are commonly deployed in cloud services and on resource-constrained edge devices. Consequently, safeguarding them is critically important. Neural network watermarking offers a practical solution to address this need by embedding a unique signature, either as a hidden bit-string or as a distinctive response to specially crafted “trigger” inputs. This allows owners to subsequently prove model ownership even if an adversary attempts to remove the watermark through attacks. In this manuscript, we adapt three state-of-the-art watermarking methods to “tiny” neural networks deployed on edge platforms by exploiting symmetry-related properties that ensure robustness and efficiency. In the context of machine learning, “tiny” is broadly used as a term referring to artificial intelligence techniques deployed in low-energy systems in the mW range and below, e.g., sensors and microcontrollers. We evaluate the robustness of the selected techniques by simulating attacks aimed at erasing the watermark while preserving the model’s original performances. The results before and after attacks demonstrate the effectiveness of these watermarking schemes in protecting neural network intellectual property without degrading the original accuracy. Full article

Deep neural network (DNN) watermarking is a suitable method for protecting the ownership of deep learning (DL) models. It secretly embeds an identifier within the model, which can be retrieved by the owner to prove ownership. In this paper, we first provide a unified framework for white-box DNN watermarking schemes that encompasses current state-of-the-art methods and outlines their theoretical inter-connections. Next, we introduce DICTION, a new white-box dynamic robust watermarking scheme derived from this framework. Its main originality lies in a generative adversarial network (GAN) strategy where the watermark extraction function is a DNN trained as a GAN discriminator, while the target model acts as a GAN generator. DICTION can be viewed as a generalization of DeepSigns, which, to the best of our knowledge, is the only other dynamic white-box watermarking scheme in the literature. Experiments conducted on four benchmark models (MLP, CNN, ResNet-18, and LeNet) demonstrate that DICTION achieves a zero bit error rate (BER) while maintaining model accuracy within 0.5% of the baseline. DICTION shows superior robustness, tolerating up to 95% weight pruning compared to 80% for existing methods, and it demonstrates complete resistance to fine-tuning and overwriting attacks where competing methods fail, with a BER of >0.3. Full article

A dual watermark and DNA image encryption based on a chaotic map is proposed. Firstly, a new discrete chaotic map is proposed, and the dynamic characteristics are analyzed. Then, the hash value changes initial conditions, and the pseudo-random sequence is generated. The encrypted copyright image is fused with the feature value of the original image and then encrypted again to form zero-watermarking, which is registered with the copyright certification authority. The zero-watermarking is taken as a robust watermark and embedded into the original image based on a chaotic sequence to ensure its invisibility. Finally, a cross-mutation DNA encryption is proposed. The experimental results verify the performance of encryption and dual watermark copyright authentication, and the ability to resist attacks. Full article

The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods. Full article

Large Language Models offer transformative capabilities but also introduce growing cybersecurity risks, particularly through their use in generating realistic phishing emails. Detecting such content is critical; however, existing methods can be resource-intensive and slow to adapt. In this research, we present a dual-layered detection framework that combines supervised learning for accurate classification with unsupervised techniques to uncover emerging threats. In controlled testing environments, our approach demonstrates strong performance. Recognising that human users are often the weakest link in information security systems, we examine historical deception patterns and psychological principles commonly exploited in phishing attacks. We also explore watermarking as a complementary method for tracing AI-generated content. Together, these strategies offer a scalable, adaptive defence against increasingly sophisticated phishing attacks driven by Large Language Models. Full article

The utilisation of 3D models in low-cost devices, such as the internet of things, virtual reality, and augmented reality, is expanding. The challenge lies in the lack of lightweight solutions for verifying the authenticity of models in the graphics library transmission format (glTF) on devices with limited resources. The glTF standard, which allows storage in glb format, is the leading standard for representing 3D assets. Despite its popularity, research on watermarking glTF models remains limited. This paper proposes a novel method for authenticating 3D models in glb format based on fragile watermarking. Additionally, an analysis was conducted to determine the impact of embedding the watermark in vertex attributes other than position on the integrity and visual quality of the model. The methodology is as follows: (1) embedding the watermark, (2) applying model modification or omitting it, and (3) verifying authenticity based on the recovered watermark. The proposed algorithm attaches a 512-bit hash-based message authentication code (HMAC) to a 3D model using the least significant bits (LSBs) modification method. The use of HMAC and LSBs has resulted in a computationally efficient algorithm that can be implemented in low-cost devices. 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

With the rapid development and convergence of systems science, cryptography, and data science, chaos-based image information security has emerged as a prominent research area, drawing considerable attention from researchers in computer science, physics, and related disciplines. This paper aims to review the fundamental concepts of chaos, as well as chaos-based image encryption, watermarking, and steganography. Building on this foundation, we analyze the evaluation standards, advancements, and applications of chaos-based image information security. Additionally, we propose several potential areas of focus for the future of chaos-based image information security, encouraging interested readers to pay attention to these crucial developments. Our analysis suggests that chaos can be effectively employed in plaintext image encryption, and that chaos-based watermarking and hiding techniques also hold promise. 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