Search Results (21)

AI-generated content (AIGC) is uniquely positioned to drive the digital transformation of professional education in the animation, comic, and game (ACG) industries. However, its collaborative application also faces initial novelty effects and user discontinuance. Existing studies often employ single-variable analytical methods, which struggle to capture the complex mechanisms influencing technology adoption. This study innovatively combines necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) and applies them to the field of ACG education. Using this mixed-method approach, it systematically explores the necessary conditions and configurational effects influencing educational users’ continuance intention to adopt AIGC tools for collaborative design learning, aiming to address existing research gaps. A survey of 312 Chinese ACG educational users revealed that no single factor constitutes a necessary condition for their continuance intention to adopt AIGC tools. Additionally, five pathways leading to high adoption intention and three pathways leading to low adoption intention were identified. Notably, the absence or insufficiency of task–technology fit, and perceived quality do not hinder ACG educational users’ willingness to actively adopt AIGC tools. This reflects the creativity-driven learning characteristics, and the flexible and diverse tool demands of the ACG discipline. The findings provide theoretical and empirical insights to enhance the effective synergy and sustainable development between ACG education and AIGC tools. Full article

The advancement of artificial intelligence (AI) technologies, including generative pre-trained transformers (GPTs) and generative models for text, image, audio, and video creation, has revolutionized content generation, creating unprecedented opportunities and critical challenges. This paper systematically examines the characteristics, methodologies, and challenges associated with detecting the synthetic content across multiple modalities, to safeguard digital authenticity and integrity. Key detection approaches reviewed include stylometric analysis, watermarking, pixel prediction techniques, dual-stream networks, machine learning models, blockchain, and hybrid approaches, highlighting their strengths and limitations, as well as their detection accuracy, independent accuracy of 80% for stylometric analysis and up to 92% using multiple modalities in hybrid approaches. The effectiveness of these techniques is explored in diverse contexts, from identifying deepfakes and synthetic media to detecting AI-generated scientific texts. Ethical concerns, such as privacy violations, algorithmic bias, false positives, and overreliance on automated systems, are also critically discussed. Furthermore, the paper addresses legal and regulatory frameworks, including intellectual property challenges and emerging legislation, emphasizing the need for robust governance to mitigate misuse. Real-world examples of detection systems are analyzed to provide practical insights into implementation challenges. Future directions include developing generalizable and adaptive detection models, hybrid approaches, fostering collaboration between stakeholders, and integrating ethical safeguards. By presenting a comprehensive overview of AIGC detection, this paper aims to inform stakeholders, researchers, policymakers, and practitioners on addressing the dual-edged implications of AI-driven content creation. Full article

The rapid advancement of Artificial Intelligence Generated Content (AIGC) has significantly accelerated the evolution of Deepfake technology, thereby introducing escalating social risks due to its potential misuse. In response to these adverse effects, researchers have developed defensive measures, including passive detection and proactive forensics. Although passive detection has achieved some success in identifying Deepfakes, it encounters challenges such as poor generalization and decreased accuracy, particularly when confronted with anti-forensic techniques and adversarial noise. As a result, proactive forensics, which offers a more resilient defense mechanism, has garnered considerable scholarly interest. However, existing proactive forensic methodologies often fall short in terms of visual quality, detection accuracy, and robustness. To address these deficiencies, we propose a novel proactive forensic approach that utilizes pseudo-Zernike moment robust watermarking. This method is specifically designed to enhance the detection and analysis of face swapping by transforming facial data into a binary bit stream and embedding this information within the non-facial regions of video frames. Our approach facilitates the detection of Deepfakes while preserving the visual integrity of the video content. Comprehensive experimental evaluations have demonstrated the robustness of this method against standard signal processing operations and its superior performance in detecting Deepfake manipulations. Full article

The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection. Full article

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change. Full article

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model’s accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm’s lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the [email protected] metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size. Full article

Artificial intelligence (AI) tools are rapidly transforming the field of traditional artistic creation, influencing painting processes and human creativity. This study explores human–AI cooperation in real-time artistic drawing by using the AIGC tool KREA.AI. Participants wear eye trackers and perform drawing tasks by adjusting the AI parameters. The research aims to investigate the impact of cross-screen and non-cross-screen conditions, as well as different viewing strategies, on cognitive load and the degree of creative stimulation during user–AI collaborative drawing. Adopting a mixed design, it examines the influence of different cooperation modes and visual search methods on creative efficacy and visual perception through eye-tracking data and creativity performance scales. The cross-screen type and task type have a significant impact on total interval duration, number of fixation points, average fixation duration, and average pupil diameter in occlusion decision-making and occlusion hand drawing. There are significant differences in the variables of average gaze duration and average pupil diameter among different task types and cross-screen types. In non-cross-screen situations, occlusion and non-occlusion have a significant impact on average gaze duration and pupil diameter. Tasks in non-cross-screen environments are more sensitive to visual processing. The involvement of AI in hand drawing in non-cross-screen collaborative drawing by designers has a significant impact on their visual perception. These results help us to gain a deeper understanding of user behaviour and cognitive load under different visual tasks and cross-screen conditions. The analysis of the creative efficiency scale data reveals significant differences in designers’ ability to supplement and improve AI ideas across different modes. This indicates that the extent of AI participation in the designer’s hand-drawn creative process significantly impacts the designer’s behaviour when negotiating design ideas with the AI. Full article

In recent years, waste pollution has become a severe threat to riparian environments worldwide. Along with the advancement of deep learning (DL) algorithms (i.e., object detection models), related techniques have become useful for practical applications. This work attempts to develop a data generation approach to generate datasets for small target recognition, especially for recognition in remote sensing images. A relevant point is that similarity between data used for model training and data used for testing is crucially important for object detection model performance. Therefore, obtaining training data with high similarity to the monitored objects is a key objective of this study. Currently, Artificial Intelligence Generated Content (AIGC), such as single target objects generated by Luma AI, is a promising data source for DL-based object detection models. However, most of the training data supporting the generated results are not from Japan. Consequently, the generated data are less similar to monitored objects in Japan, having, for example, different label colors, shapes, and designs. For this study, the authors developed a data generation approach by combining social media (Clean-Up Okayama) and single-image-based 3D model generation algorithms (e.g., InstantMesh) to provide a reliable reference for future generations of localized data. The trained YOLOv8 model in this research, obtained from the S2PS (Similar to Practical Situation) AIGC dataset, produced encouraging results (high F1 scores, approximately 0.9) in scenario-controlled UAV-based riparian PET bottle waste identification tasks. The results of this study show the potential of AIGC to supplement or replace real-world data collection and reduce the on-site work load. Full article

The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers’ willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers’ perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers’ attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising. Full article

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females. Full article

The development of new artificial intelligence-generated content (AIGC) technology creates new opportunities for the digital transformation of education. Teachers’ willingness to adopt AIGC technology for collaborative teaching is key to its successful implementation. This study employs the TAM and TPB to construct a model analyzing teachers’ acceptance of AIGC technology, focusing on the influencing factors and differences across various educational stages. The study finds that teachers’ behavioral intentions to use AIGC technology are primarily influenced by perceived usefulness, perceived ease of use, behavioral attitudes, and perceived behavioral control. Perceived ease of use affects teachers’ willingness both directly and indirectly across different groups. However, perceived behavioral control and behavioral attitudes only directly influence university teachers’ willingness to use AIGC technology, with the impact of behavioral attitudes being stronger than that of perceived behavioral control. The empirical findings of this study promote the rational use of AIGC technology by teachers, providing guidance for encouraging teachers to actively explore the use of information technology in building new forms of digital education. Full article

Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges. Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers’ behavioral intentions, employing structural equation modeling for validation. The findings reveal the following: (1) designer trust is the primary factor influencing their behavioral choices; (2) different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks; (3) only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated; and (4) only by effectively safeguarding designers’ legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions. This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading. Full article

The present research aims to explore the dual potential of artificial intelligence-generated content (AIGC) technology in the esthetic reproduction of Ming-style furniture and its innovative design while promoting sustainable practices and cultural heritage preservation. For this purpose, a combination of methodologies integrating the principles of grounded theory, empirical research, sustainable design, and design practice and evaluation techniques is employed. The results are as follows: First, the three-level coding method in grounded theory is used to construct a multi-dimensional esthetic feature library of Ming-style furniture, including 6 esthetic feature dimensions and 102 groups of esthetic elements. Second, a set of databases specifically for Ming-style furniture is developed based on the Midjourney platform. The AIGC exclusive toolkit for furniture (MFMP) contains a language package of 61 keywords and a basic formula for Ming-style furniture design. The MFMP toolkit accurately reproduces Ming-style furniture esthetics through empirical validation. Finally, combined with sustainable design principles, a new path is explored in order to utilize the MFMP toolkit for the sustainable and innovative design of new Chinese-style furniture. The research results demonstrate that AIGC enhances traditional and modern furniture design, offering tools for industry growth in a sustainable way and preserving cultural heritage. Full article

The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven method utilizes a wide range of knowledge resources, such as historical documents, architectural drawings, and photographs, commonly used to guide and optimize the conservation, restoration, and management of architectural heritage. Recently, the emergence of artificial intelligence-generated content (AIGC) technologies has provided new solutions for creating architectural facades, introducing a new research paradigm to the renovation plans for historic districts with their variety of options and high efficiency. In this study, we propose a workflow combining Grasshopper with Stable Diffusion: starting with Grasshopper to generate concise line drawings, then using the ControlNet and low-rank adaptation (LoRA) models to produce images of traditional Minnan architectural facades, allowing designers to quickly preview and modify the facade designs during the renovation of traditional architectural clusters. Our research results demonstrate Stable Diffusion’s precise understanding and execution ability concerning architectural facade elements, capable of generating regional traditional architectural facades that meet architects’ requirements for style, size, and form based on existing images and prompt descriptions, revealing the immense potential for application in the renovation of traditional architectural groups and historic districts. It should be noted that the correlation between specific architectural images and proprietary term prompts still requires further addition due to the limitations of the database. Although the model generally performs well when trained on traditional Chinese ancient buildings, the accuracy and clarity of more complex decorative parts still need enhancement, necessitating further exploration of solutions for handling facade details in the future. Full article

Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results. Full article