Search Results (30)

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

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

With the rapid development of electronic business, there has been a surge in incidents of trademark infringement, making it imperative to improve the accuracy of trademark retrieval systems as a key measure to combat such illegal behaviors. Evidently, the textual information encompassed within trademarks substantially influences the precision of search results. Considering the diversity of trademark text and the complexity of its design elements, accurately locating and analyzing this text poses a considerable challenge. Against this background, this research has developed an original self-prompting text removal model, denoted as “Self-prompting Trademark Text Removal Based on Multi-scale Texture Aggregation” (abbreviated as MTF-STTR). This model astutely applies a text detection network to automatically generate the required input cues for the Segment Anything Model (SAM) while incorporating the technological benefits of diffusion models to attain a finer level of trademark text removal. To further elevate the performance of the model, we introduce two innovative architectures to the text detection network: the Integrated Differentiating Feature Pyramid (IDFP) and the Texture Fusion Module (TFM). These mechanisms are capable of efficiently extracting multilevel features and multiscale textual information, which enhances the model’s stability and adaptability in complex scenarios. The experimental validation has demonstrated that the trademark text erasure model designed in this paper achieves a peak signal-to-noise ratio as high as 40.1 dB on the SCUT-Syn dataset, which is an average improvement of 11.3 dB compared with other text erasure models. Furthermore, the text detection network component of the designed model attains an accuracy of up to 89.9% on the CTW1500 dataset, representing an average enhancement of 10 percentage points over other text detection networks. 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

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

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

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network’s recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos. Full article

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data. 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

The detection of image similarity is critical to trademark (TM) legal registration and court judgment on infringement cases. Meanwhile, there are great challenges regarding the annotation of similar pairs and model generalization on rapidly growing data when deep learning is introduced into the task. The research idea of metric learning is naturally suited for the task where similarity of input is given instead of classification, but current methods are not targeted at the task and should be upgraded. To address these issues, loss-driven model training is introduced, and a hybrid-margin softmax (HMS) is proposed exactly based on the peculiarity of TM images. Two additive penalty margins are attached to the softmax to expand the decision boundary and develop greater tolerance for slight differences between similar TM images. With the HMS, a Siamese neural network (SNN) as the feature extractor is further penalized and the discrimination ability is improved. Experiments demonstrate that the detection model trained on HMS can make full use of small numbers of training data and has great discrimination ability on bigger quantities of test data. Meanwhile, the model can reach high performance with less depth of SNN. Extensive experiments indicate that the HMS-driven model trained completely on TM data generalized well on the face recognition (FR) task, which involves another type of image data. Full article

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work. Full article

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. This work proposes an ensemble neural network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. First, we performed image classification and objection detection learning per design right using acclaimed existing models with high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiplies the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was performed an order of magnitude faster than the single model. The ENN maintained a high level of accuracy as it was scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as customs at the border that deal with a swarm of products. Full article

Technologies providing copyright-infringing IPTV content are commonly used as an illegal alternative to legal IPTV subscriptions and services, as they usually have lower monetary costs and can be more convenient for users who follow content from different sources. These infringing IPTV technologies may include websites, software, software add-ons, and physical set-top boxes. Due to the free or low cost of illegal IPTV technologies, illicit IPTV content providers will often resort to intrusive advertising, scams, and the distribution of malware to increase their revenue. We developed an automated solution for collecting and analysing malware from illegal IPTV technologies and used it to analyse a sample of illicit IPTV websites, application (app) stores, and software. Our results show that our IPTV Technologies Malware Analysis Framework (IITMAF) classified 32 of the 60 sample URLs tested as malicious compared to running the same test using publicly available online antivirus solutions, which only detected 23 of the 60 sample URLs as malicious. Moreover, the IITMAF also detected malicious URLs and files from 31 of the sample’s websites, one of which had reported ransomware behaviour. Full article

Focusing on the moral hazard of third-party environmental service providers in monitoring and controlling the emission of pollutants by enterprises, this paper takes the third-party governance of environmental pollution under the incentive-and-constraint mechanism as its research object. It also constructs a game model involving emission-producing enterprises producing emissions, third-party environmental service providers, and local governments. Adopting this evolutionary game model, this paper analyzes the mechanism of local government’s role in effectively resolving the moral hazard between emission-producing enterprises producing emissions and third-party environmental service providers by exploring the conditions of spontaneous cooperation between emission-producing enterprises producing emissions and third-party environmental service providers. This paper provides a possible solution to the problem of emission-producing enterprises or third-party environmental service providers stealing and leaking emissions, as well as collusion between the two. The study presents two major findings. (1) There are three possible scenarios of breach of contract: unilateral breach by third-party environmental service providers, unilateral breach by emissions-producing enterprises, and collusion between the two. When a third-party environmental service provider unilaterally breaches a contract, emission-producing enterprises have regulatory responsibilities toward them. In such cases, local governments should reduce the penalties imposed on emission-producing enterprises. This measure would decrease the willingness of these enterprises to allocate a higher proportion of collusion payments to third-party environmental service providers. However, it would simultaneously provide a new avenue through which third-party environmental service providers would gain benefits, thereby increasing their expected gains from collusion. This would create a new game between the two parties, leading to the failure of collusion negotiations. (2) The efficacy of incentive-constraint mechanisms is influenced by the severity of contractual breaches, represented by the magnitude of stealing and leaking emissions. When false emissions reduction is at a high level, increasing the incentives for emission-producing enterprises and third-party environmental service providers cannot effectively prevent collusion; when the level is moderate, incentives for third-party environmental service providers can effectively prevent collusion, but incentives for emission-producing enterprises cannot; when the level is low, increasing the incentives for emission-producing enterprises and third-party environmental service providers can help prevent collusion. (3) When emission-producing enterprises engage in unilateral discharge, if a local government’s incentive for third-party environmental service providers exceeds the benefits it can obtain from regulating the discharged amount, third-party environmental service providers tacitly approve the company’s discharge behavior. However, with the strengthening of local government regulations, emission-producing enterprises tend to engage in more clandestine discharging of pollutants to obtain greater rewards. This practice infringes upon the revenue of third-party environmental service providers, as their earnings are positively correlated with the amount of pollution abated. Third-party environmental service providers no longer acquiesce to corporate emissions theft, resulting in an increase in the probability of the detection of emission-producing enterprises’ illicit discharges; in this way, the behavior of these enterprises is regulated. Full article

With the advent of the digital age, traditional lifestyle activities, such as reading books, referencing recipes, and enjoying music, have progressively transitioned from offline to online. However, numerous issues plague the conventional approach to digital copyright protection. This is especially true in the realm of recipe protection, where the rights and interests of original creators are inadequately safeguarded due to the widespread dissemination of a large number of recipes on the Internet. This primarily stems from the high costs of gathering evidence, incomplete coverage of evidence collection, and the inability to identify and halt infringement activities in a timely manner during the process of traditional digital copyright protection. Therefore, this study designs and implements a blockchain-based digital recipe copyright protection scheme to address the issues of insufficient legal evidence and cumbersome processes in traditional digital copyright protection. First, we enhance standard short text similarity calculation method SimHash, boosting the accuracy of text similarity detection. We then utilize the decentralization, immutability, time-stamping, traceability, and smart contract features of blockchain technology for data privacy protection. We employ the Interplanetary File System (IPFS) to store raw data, thereby ensuring user privacy and security. Lastly, we improve the proxy voting node selection in the existing delegated proof of stake (DPOS) consensus mechanism. According thorough evaluation and empirical analysis, the scheme effectively improves the accuracy of text similarity detection. Simultaneously, the enhanced DPOS mechanism effectively rewards nodes with excellent performance and penalizes nodes exhibiting malicious behavior. In this study, we successfully designed and implemented an innovative digital recipe copyright protection scheme. This scheme effectively enhances the accuracy of text similarity detection; ensures the privacy and security of user data; and, through an enhanced DPOS mechanism, rewards well-performing nodes while penalizing those exhibiting malicious behavior. Full article