Search Results (36)

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

This study systematically evaluates website blocking as both an anti-piracy enforcement mechanism and a cybersecurity control, analyzing its effectiveness in reducing piracy across four Southeast Asian jurisdictions with distinct legal frameworks, assessing blocking speed, procedural barriers, and circumvention tactics, providing new empirical insights for policymakers and cybersecurity practitioners. Using a quasi-experimental design during the COVID-19 pandemic, this research examines the impact of website blocking measures in Indonesia, Vietnam, Malaysia, and Singapore. For the first time, the findings reveal that swift, systematic website blocking—exemplified by Indonesia—serves as an effective cybersecurity control, significantly reducing access to infringing content while redirecting traffic toward legitimate platforms. Jurisdictions with procedural delays and inconsistent enforcement, however, demonstrate limited efficacy, highlighting the need for dynamic responses to evolving threats such as domain hopping and proxy servers. The findings inform broader cybersecurity applications like network segmentation, access control, and threat intelligence. This work links traditional copyright enforcement to proactive incident detection and response strategies, providing insights into broader applications for cybersecurity, such as network segmentation, access control, and threat intelligence. Full article

Due to the continuous increase in copyright infringement cases of video content, the economic losses of copyright owners continue to rise. To improve the efficiency of plagiarism detection in video content, in this paper, we propose region-based video feature learning. The first innovation of this paper lies in the combination of temporal positional encoding and attention mechanisms to extract global features for weakly supervised model training. Self- and cross-attention mechanisms are combined to enhance similar features within and between videos by incorporating position coding to capture timing relationships between video frames. Global classification description is embedded for capturing global spatiotemporal information and combined with a weak supervised loss for model training. The second innovation is the frame sequence similarity calculation, which is composed of Chamfer similarity, coordinate attention mechanism, and residual connection, to aggregate similarity scores between videos. Experimental results show that the proposed method can achieve the mAP of 0.907 on the short video dataset from Douyin. The proposed method outperforms frame-level and video-level features in achieving higher detection accuracy, and further contributes to the improvement of video content plagiarism detection performance. Full article

The emergence of generative AI presents complex challenges to existing copyright regimes, particularly concerning the large-scale use of copyrighted materials in model training. Legal disputes across jurisdictions highlight the urgent need for a balanced, principle-based framework that protects the rights of creators while fostering innovation. In China, a regulatory approach of “moderate leniency” has emerged—emphasizing control over downstream AI-generated content (AIGC) while adopting a more permissive stance toward upstream training. This model upholds the idea–expression dichotomy, rejecting theories such as “retained expression” or “retained style”, which improperly equate ideas with expressions. A critical legal distinction lies between real-time training, which is ephemeral and economically insignificant, and non-real-time training, which involves data retention and should be assessed under fair use test. A fair use exception specific to AI training is both timely and justified, provided it ensures equitable sharing of technological benefits and addresses AIGC’s potential substitutive impact on original works. Furthermore, technical processes like format conversion and machine translation do not infringe derivative rights, as they lack human creativity and expressive content. Even when training involves broader use, legitimacy may be established through the principle of technical necessity within the reproduction right framework. Full article

The issue of image copyright infringement is prevalent in current e-commerce activities. Users employ methods such as image cropping, compression, and noise addition, making it difficult for traditional copyright detection technologies to identify and track infringements. This study proposes an image copyright registration, protection, and management method based on artificial intelligence and blockchain technology, aiming to address the current challenges of low accuracy in digital copyright infringement judgment, the vulnerability of image fingerprints stored on the chain to tampering, the complexity of encryption algorithms and key acquisition methods through contract calls, and the secure storage of image information during data circulation. The research combines artificial intelligence technology with traditional blockchain technology to overcome the inherent technical barriers of blockchain. It introduces an originality detection model based on deep learning technology after conducting both off-chain and on-chain detection of unidentified images, providing triple protection for digital image copyright infringement detection and enabling efficient active defense and passive evidence storage. Additionally, the study improves upon the traditional image perceptual hashing in blockchain, which has poor robustness, by adding chaotic encryption sequences to protect the image data on the chain, and its effectiveness has been verified through experiments. Ultimately, the research hopes to provide e-commerce entities with an effective and feasible digital copyright protection and management solution, safeguarding their intellectual property rights and fostering a legal and reasonable competitive environment in e-commerce. 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

The rapid advancement of AI-generated content (AIGC) has significantly improved the realism and accessibility of synthetic images. While large image generation models offer immense potential in creative industries, they also introduce serious challenges, including copyright infringement, content authentication, and the traceability of generated images. Digital watermarking has emerged as a promising approach to address these concerns by embedding imperceptible yet detectable signatures into generated images. This survey provides a comprehensive review of three core areas: (1) the evolution of image generation technologies, highlighting key milestones such as the transition from GANs to diffusion models; (2) traditional and state-of-the-art digital image watermarking algorithms, encompassing spatial domain, transform domain, and deep learning-based approaches; (3) watermarking methods specific to AIGC, including ownership authentication of AI model and diffusion model, and watermarking of AI-generated images. Additionally, we examine common performance evaluation metrics used in this field, such as watermark capacity, watermark detection accuracy, fidelity, and robustness. Finally, we discuss the unresolved issues and propose several potential directions for future research. We look forward to this paper offering valuable reference for academics in the field of AIGC watermarking and related fields. Full article

Deep learning-based remote sensing object detection (RSOD) models have been widely deployed and commercialized. The commercialization of RSOD models requires the ability to protect their intellectual property (IP) across different platforms and sales channels. However, RSOD models currently face threats related to illegal copying on untrusted platforms or resale by dishonest buyers. To address this issue, we propose a dual-model watermarking scheme for the copyright verification and leakage tracing of RSOD models. First, we construct trigger samples using an object generation watermark trigger and train them alongside clean samples to implement black-box watermarking. Then, fingerprint information is embedded into a small subset of the model’s critical weights, using a fine-tuning and loss-guided approach. At the copyright verification stage, the presence of a black-box watermark can be confirmed through using the suspect model’s API to make predictions on the trigger samples, thereby determining whether the model is infringing. Once infringement is confirmed, fingerprint information can be further extracted from the model weights to identify the leakage source. Experimental results demonstrate that the proposed method can effectively achieve the copyright verification and traceability of RSOD models without affecting the performance of primary tasks. The watermark shows good robustness against fine-tuning and pruning attacks. Full article

Combining blockchain technology with digital watermarking presents an efficient solution for safeguarding vector map files. However, the large data volume and stringent confidentiality requirements of vector maps pose significant challenges for direct registration on blockchain platforms. To overcome these limitations, this paper proposes a blockchain-based copyright protection model utilizing unique identifiers (BCPM-UI). The model employs a distance ratio-based quantization watermarking algorithm to embed watermark information into vector maps and then generates unique identifiers based on their topological and geometric parameters. These identifiers, rather than the vector maps themselves, are securely registered on the blockchain. To ensure reliable copyright verification, a bit error rate (BER)-based matching algorithm is introduced, enabling accurate comparison between the unique identifiers of suspected infringing data and those stored on the blockchain. Experimental results validate the model’s effectiveness, demonstrating the high uniqueness and robustness of the identifiers generated. Additionally, the proposed approach reduces blockchain storage requirements for map data by a factor of 200, thereby meeting confidentiality standards while maintaining practical applicability in terms of copyright protection for vector maps. Full article

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments. Full article

High-quality datasets are essential for training high-performance models, while the process of collection, cleaning, and labeling is costly. As a result, datasets are considered valuable intellectual property. However, when security mechanisms are symmetry-breaking, creating exploitable vulnerabilities, unauthorized use or data leakage can infringe on the copyright of dataset owners. In this study, we design a method to mount clean-label dataset watermarking based on trigger optimization, aiming to protect the copyright of the dataset from infringement. We first perform iterative optimization of the trigger based on a surrogate model, with targets class samples guiding the updates. The process ensures that the optimized triggers contain robust feature representations of the watermark target class. A watermarked dataset is obtained by embedding optimized triggers into randomly selected samples from the watermark target class. If an adversary trains a model with the watermarked dataset, our watermark will manipulate the model’s output. By observing the output of the suspect model on samples with triggers, it can be determined whether the model was trained on the watermarked dataset. The experimental results demonstrate that the proposed method exhibits high imperceptibility and strong robustness against pruning and fine-tuning attacks. Compared to existing methods, the proposed method significantly improves effectiveness at very low watermarking rates. Full article

Images are extensively utilized in educational materials due to their efficacy in conveying complex concepts. However, unauthorized use of images frequently results in legal issues related to copyright infringement. To mitigate this problem, we introduce a dual-module system specifically designed for educators. The first module, a copyright infringement detection system, employs deep learning techniques to verify the copyright status of images. It utilizes a Convolutional Variational Autoencoder (CVAE) model to extract significant features from copyrighted images and compares them against user-provided images. If infringement is detected, the second module, an image retrieval system, recommends alternative copyright-free images using a Vision Transformer (ViT)-based hashing model. Evaluation on benchmark datasets demonstrates the system’s effectiveness, achieving a mean Average Precision (mAP) of 0.812 on the Flickr25k dataset. Additionally, a user study involving 65 teachers indicates high satisfaction levels, particularly in addressing copyright concerns and ease of use. Our system significantly aids educators in creating educational materials that comply with copyright regulations. Full article

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI’s ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions. Full article

This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, capacity, and robustness. The novelty of this research lies in the introduction of the F5A method, which utilizes optimized quantization tables to significantly increase the capacity for concealed information while ensuring high-quality image retention and resistance to unauthorized content recovery. The F5A method integrates cryptographic keys and features to detect and prevent copyright infringement in real time. Experimental evaluations demonstrate that the F5A method improves the mean square error and peak signal-to-noise ratio indices by 1.716 and 1.121 times, respectively, compared to the traditional F5 algorithm. Additionally, it increases the steganographic capacity by up to 1.693 times for smaller images and 1.539 times for larger images. These results underscore the effectiveness of the F5A method in enhancing digital media security and copyright protection. Full article

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators’ rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations. Full article