Search Results (248)

Most oblique photography 3D model watermarking algorithms only support limited data recovery or fail to restore the original model, falling short of meeting diverse user needs. Consequently, this study introduces a novel reversible watermarking scheme specifically tailored for oblique photographic 3D models, which is designed to adjust the accuracy of model recovery freely. Firstly, considering the global stability of the oblique photography 3D model, the feature points are extracted by utilizing the mean angle between vertex normals. Secondly, a mapping is established based on the ratio of distances between feature points and non-feature points. Then, the vertices are grouped, with each group consisting of one feature point and several non-feature points. Finally, by using the feature point as the origin, a spherical coordinate system is constructed for each group. The watermark information is embedded by modifying the radius in the spherical coordinate system. In the process of extracting watermarks, watermarks can be extracted from different radius ranges, thereby achieving a controllable error in model recovery. Experimental results demonstrate that this approach exhibits significant advantages in reversibility and controllable restoration accuracy, achieving error-free extraction under both translation and rotation attacks. Compared to existing algorithms, it achieves average improvements of 0.121 and 0.298 under cropping and simplification attacks, respectively, showcasing enhanced robustness. This enables it to meet better diverse user demands for watermarking and model restoration in oblique photography 3D models. Full article

In the digital era, protecting the integrity and ownership of digital content is increasingly crucial, particularly against unauthorized copying and tampering. Traditional watermarking techniques often struggle to remain robust under various image manipulations, leading to a need for more resilient methods. To address this challenge, we propose a novel watermarking technique that integrates the Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Schur matrix factorization to embed a QR code as a watermark into digital images. Our method was rigorously tested across a range of common image attacks, including histogram equalization, salt-and-pepper noise, ripple distortions, smoothing, and extensive cropping. The results demonstrate that our approach significantly outperforms existing methods, achieving high normalized correlation (NC) values such as 0.9949 for histogram equalization, 0.9846 for salt-and-pepper noise (2%), 0.96063 for ripple distortion, 0.9670 for smoothing, and up to 0.9995 under 50% cropping. The watermark consistently maintained its integrity and scannability under all tested conditions, making our method a reliable solution for enhancing digital copyright protection. Full article

Image watermarking is an essential technique for protecting the copyright of digital images. This paper proposes a novel color image watermarking algorithm based on the Walsh–Hadamard Transform (WHT). By analyzing the differences among WHT coefficients, an asymmetric embedding position selection strategy is designed to enhance the robustness of the algorithm. Specifically, the color image is first separated into red (R), green (G), and blue (B) channels, each of which is divided into non-overlapping 4 × 4 blocks. Then, suitable embedding regions are selected based on the entropy of each block. Finally, the optimal embedding positions are determined by comparing the differences between WHT coefficient pairs. To ensure watermark security, the watermark is encrypted using Logistic chaotic map prior to embedding. During the extraction phase, the watermark is recovered using the chaotic key and the pre-stored embedding position information. Extensive simulation experiments are conducted to evaluate the effectiveness of the proposed algorithm. The comparative results demonstrate that the proposed method maintains high imperceptibility while exhibiting superior robustness against various attacks, outperforming existing state-of-the-art approaches in overall performance. Full article

This study presents a novel framework for digital copyright management that integrates AI-enhanced perceptual hashing, blockchain technology, and digital watermarking to address critical challenges in content protection and verification. Traditional watermarking approaches typically employ content-independent metadata and rely on centralized authorities, introducing risks of tampering and operational inefficiencies. The proposed system utilizes a pre-trained convolutional neural network (CNN) to generate a robust, content-based perceptual hash value, which serves as an unforgeable watermark intrinsically linked to the image content. This hash is embedded as a QR code in the frequency domain and registered on a blockchain, ensuring tamper-proof timestamping and comprehensive traceability. The blockchain infrastructure further enables verification of multiple watermark sequences, thereby clarifying authorship attribution and modification history. Experimental results demonstrate high robustness against common image modifications, strong discriminative capabilities, and effective watermark recovery, supported by decentralized storage via the InterPlanetary File System (IPFS). The framework provides a transparent, secure, and efficient solution for digital rights management, with potential future enhancements including post-quantum cryptography integration. Full article

The advancements in communication and information technologies have substantially enabled the extensive distribution and modification of high-resolution color images. Although this accessibility provides many advantages, it also presents risks related to security. Specifically, when image modification is conducted with malicious intent, exceeding typical artistic or enhancement objectives, it can cause significant moral or economic harm to the image owner. To address this security requirement, this study presents an innovative semi-fragile watermarking algorithm designed specifically for high-resolution color images. The proposed method utilizes Discrete Cosine Transform domain watermarking implemented via Quantization Index Modulation with Dither Modulation. It incorporates several elements, such as convolutional encoding, a denoising convolutional neural network, and a very deep super-resolution neural network. This comprehensive strategy aims to provide ownership verification using a logo watermark, in conjunction with tamper detection and content self-recovery mechanisms. The self-recovery criterion is determined using a thumbnail image, created by downscaling to standard definition and applying JPEG2000 lossy compression. The resultant multifunctional design enhances the overall security of the information. Experimental validation confirms the enhanced imperceptibility, robustness, and capacity of the proposed method. Its efficacy was additionally corroborated through comparative analyses using contemporary state-of-the-art algorithms. Full article

Quantum watermarking is a technique that embeds specific information into a quantum carrier for the purpose of digital copyright protection. In this paper, we propose a novel color image watermarking algorithm that integrates quantum discrete wavelet transform with Sinusoidal–Tent mapping and baker mapping. Initially, chaotic sequences are generated using Sinusoidal–Tent mapping to determine the channels suitable for watermark embedding. Subsequently, a one-level quantum Haar wavelet transform is applied to the selected channel to decompose the image. The watermarked image is then scrambled via discrete baker mapping, and the scrambled image is embedded into the High-High subbands. The invisibility of the watermark is evaluated by calculating the peak signal-to-noise ratio, Structural similarity index measure, and Learned Perceptual Image Patch Similarity, with comparisons made against the color histogram. The robustness of the proposed algorithm is assessed through the calculation of Normalized Cross-Correlation. In the simulation results, PSNR is close to 63, SSIM is close to 1, LPIPS is close to 0.001, and NCC is close to 0.97. This indicates that the proposed watermarking algorithm exhibits excellent visual quality and a robust capability to withstand various attacks. Additionally, through ablation study, the contribution of each technique to overall performance was systematically evaluated. Full article

Ensuring the integrity of medical images is essential to securing clinical workflows, telemedicine platforms, and healthcare IoT environments. Existing watermarking and reversible data-hiding approaches often modify pixel intensities, reducing diagnostic fidelity, introducing embedding constraints, or causing instability under compression and format conversion. This work proposes a distortion-free, non-embedding authentication framework that leverages the inherent stability of the most significant bit (MSB) patterns in the Non-Region of Interest (NROI) to construct a secure and tamper-sensitive reference for the diagnostic Region of Interest (ROI). The ROI is partitioned into fixed blocks, each producing a 256-bit SHA-256 signature. Instead of embedding this signature, each hash bit is mapped to an NROI pixel whose MSB matches the corresponding bit value, and only the encrypted coordinates of these pixels are stored externally in a secure database. During verification, hashes are recomputed and compared bit-by-bit with the MSB sequence extracted from the referenced NROI coordinates, enabling precise block-level tamper localization without modifying the image. Extensive experiments conducted on MRI (OASIS), X-ray (ChestX-ray14), and CT (CT-ORG) datasets demonstrate the following: (i) perfect zero-distortion fidelity; (ii) stable and deterministic MSB-class mapping with abundant coordinate diversity; (iii) 100% detection of intentional ROI tampering with no false positives across the six clinically relevant manipulation types; and (iv) robustness to common benign Non-ROI operations. The results show that the proposed scheme offers a practical, secure, and computationally lightweight solution for medical image integrity verification in PACS systems, cloud-based archives, and healthcare IoT applications, while avoiding the limitations of embedding-based methods. Full article

The increasing prevalence of deep learning models in industry has highlighted the critical need to protect the intellectual property (IP) of these models, especially generative adversarial networks (GANs) capable of synthesizing realistic data. Traditional IP protection methods, such as watermarking model parameters (white-box) or verifying outputs (black-box), are insufficient against non-public misappropriation. To address these limitations, we introduce EGAN (Encrypted GANs), which secures GAN models by embedding a novel self-adversarial mechanism. This mechanism is trained to actively maximize the feature divergence between authorized and unauthorized inputs, thereby intentionally corrupting the outputs from non-key inputs and preventing unauthorized operation. Our methodology utilizes key-based transformations applied to GAN inputs and incorporates a generator loss regularization term to enforce model protection without compromising performance. This technique is compatible with existing watermark-based verification methods. Extensive experimental evaluations reveal that EGAN maintains the generative capabilities of original GAN architectures, including DCGAN, SRGAN, and CycleGAN, while exhibiting robust resistance to common attack strategies such as fine-tuning. Compared with prior work, EGAN provides comprehensive IP protection by ensuring unauthorized users cannot achieve desired outcomes, thus safeguarding both the models and their generated data. Full article

Existing model watermarking methods fail to provide adequate protection for edge intelligence models. This paper innovatively integrates the characteristics of model fingerprinting, proposing a model watermarking method named FingerMarks that enables both model attribution and traceability of edge node users. The method initially constructs a uniform trigger set and an encoding scheme through fingerprint extraction, which effectively distinguishes the host model from independently trained models. Based on the encoding scheme, distinct user IDs are converted and mapped into specific labels, thereby generating distinct watermark-embedded trigger sets. Watermarks are embedded using a progressive adversarial training strategy. Comprehensive evaluation across multiple datasets confirms the method’s performance, uniqueness, and robustness. Experimental results show that FingerMarks effectively identifies the watermarked model while maintaining superior robustness compared to state-of-the-art alternatives. Full article

Using an attack-simulation module is a well-recognized approach to improving the robustness of end-to-end neural-network-based data-hiding schemes. However, most proposed attack simulators are limited in the types of attacks they cover, usually handling only a basic set of digital transformations. Real, in-demand use cases for data-hiding methods may involve modifications that cannot be modeled by basic digital transformations such as filtering, noise, or compression. In the screen-cam scenario, when an image containing hidden data is displayed on a screen and captured by a camera, the distortions are much more complex and typically require manual experiments that manipulate physical objects in order to replicate. This hinders both the process of creating applicable data-hiding schemes for this scenario and evaluating their effectiveness. In this work, we propose a generator neural network to simulate screen-cam distortions that can replace the manual, time-consuming operations of replicating this attack in the real world, and we show how it can be used to improve the robustness of an existing data-hiding scheme. In our example, we increased robustness by 15% in terms of bit error rate. 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

Digital image watermarking is a vital tool for copyright protection and content authentication. However, most existing methods perform well only under single noise types, while real-world applications often involve composite noises with multiple distortions, leading to poor robustness. To address this issue, we propose a robust image watermarking scheme. To improve performance under combined noise conditions, a two-stage training strategy is introduced: in the first stage, noise intensity increases gradually to stabilize training; in the second stage, mixed strong noises are applied to enhance generalization against complex attacks. Specifically, a strength-balanced watermark optimization algorithm is employed during the testing stage to improve visual quality while maintaining strong robustness. Furthermore, to improve robustness against JPEG compression, we adopt a differentiable fine-grained JPEG module that accurately simulates real compression and enables gradient backpropagation during training. Experimental results demonstrate the superiority of the proposed method under various single and combined distortions. Under noise-free conditions, it achieves 0% bit error rate and 53.55 dB PSNR. Under composite distortions, our scheme maintains a low average BER of 2.40% and a PSNR of 42.70 dB. Full article

The exponential advancement of the Internet of Things and artificial intelligence technologies has significantly accelerated digital content generation and dissemination, intensifying challenges in copyright protection, identity theft, and privacy breaches. Traditional digital watermarking techniques, constrained by vulnerabilities to geometric attacks and perceptual distortions, fail to meet the demands of modern complex application scenarios. To address these limitations, this paper proposes a robust watermarking algorithm based on quaternion Gyrator transform and neighborhood coefficient statistical features, designed to enhance copyright protection efficacy. The methodology involves three key innovations: (1) The host image is partitioned into non-overlapping sub-blocks, with an inhomogeneity metric calculated from local texture and edge characteristics to prioritize embedding sequence optimization; (2) quaternion Gyrator transform is applied to each sub-block, where the real component of transformed coefficients is utilized as the feature carrier, harnessing the geometric invariance of quaternion transformations to mitigate distortions induced by rotational attacks; (3) Integration of an Improved Uniform Log-Polar Mapping algorithm to embed synchronization markers, reinforcing resistance to geometric attacks by preserving structural consistency under affine transformations. Prior to embedding, dynamic statistical analysis of neighborhood coefficients adjusts watermark intensity, ensuring compatibility with human visual system masking properties. Experimental results demonstrate dual advantages: The PSNR of the proposed method is 41.4921, showing good invisibility. The average NC value remains at around 0.9, demonstrating good robustness. The effectiveness and practicability of the algorithm in a complex attack environment have been verified. Full article

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

With the widespread application of generative adversarial networks (GANs) in image generation and content creation, their model architectures and training outcomes have become valuable intellectual property assets. However, in practical deployment, image generative models are vulnerable to surrogate model attacks, posing significant risks to copyright ownership and commercial interests. To address this issue, this paper proposes a novel copyright protection scheme for image generative models with a symmetric embedding–retrieval watermark architecture in GANs focused on defending against surrogate model attacks. Unlike traditional model encryption or architectural constraint strategies, the proposed approach integrates a watermark embedding module directly into the image generative network, enabling generated images to implicitly carry copyright identifiers. Leveraging a symmetric design between the embedding and retrieval processes, the system ensures that, under surrogate model attacks, the original model’s identity can be reliably verified by extracting the embedded watermark from the generated outputs. The implementation comprises three key modules—feature extraction, watermark embedding, and watermark retrieval—forming an end-to-end, balanced embedding–retrieval pipeline. Experimental results demonstrate that this approach achieves efficient and stable watermark embedding and retrieval without compromising generation quality, exhibiting high robustness, traceability, and practical applicability, thereby offering a viable and symmetric solution for intellectual property protection in image generative networks. Full article