3,513 research outputs found
A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions
With the widespread use of large artificial intelligence (AI) models such as
ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is
leading a paradigm shift in content creation and knowledge representation. AIGC
uses generative large AI algorithms to assist or replace humans in creating
massive, high-quality, and human-like content at a faster pace and lower cost,
based on user-provided prompts. Despite the recent significant progress in
AIGC, security, privacy, ethical, and legal challenges still need to be
addressed. This paper presents an in-depth survey of working principles,
security and privacy threats, state-of-the-art solutions, and future challenges
of the AIGC paradigm. Specifically, we first explore the enabling technologies,
general architecture of AIGC, and discuss its working modes and key
characteristics. Then, we investigate the taxonomy of security and privacy
threats to AIGC and highlight the ethical and societal implications of GPT and
AIGC technologies. Furthermore, we review the state-of-the-art AIGC
watermarking approaches for regulatable AIGC paradigms regarding the AIGC model
and its produced content. Finally, we identify future challenges and open
research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks
Information Forensics and Security: A quarter-century-long journey
Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends
Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage
Deep learning methods have impacted almost every research field,
demonstrating notable successes in medical imaging tasks such as denoising and
super-resolution. However, the prerequisite for deep learning is data at scale,
but data sharing is expensive yet at risk of privacy leakage. As cutting-edge
AI generative models, diffusion models have now become dominant because of
their rigorous foundation and unprecedented outcomes. Here we propose a latent
diffusion approach for data synthesis without compromising patient privacy. In
our exemplary case studies, we develop a latent diffusion model to generate
medical CT, MRI and PET images using publicly available datasets. We
demonstrate that state-of-the-art deep learning-based
denoising/super-resolution networks can be trained on our synthetic data to
achieve image quality equivalent to what the same network can achieve after
being trained on the original data (the p values well exceeding the threshold
of 0.05). In our advanced diffusion model, we specifically embed a safeguard
mechanism to protect patient privacy effectively and efficiently. Consequently,
every synthetic image is guaranteed to be different by a pre-specified
threshold from the closest counterpart in the original patient dataset. Our
approach allows privacy-proof public sharing of diverse big datasets for
development of deep models, potentially enabling federated learning at the
level of input data instead of local network weights.Comment: 30 pages, 3 figure
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
The intersection of the Foundation Model (FM) and Federated Learning (FL)
provides mutual benefits, presents a unique opportunity to unlock new
possibilities in AI research, and address critical challenges in AI and
real-world applications. FL expands the availability of data for FMs and
enables computation sharing, distributing the training process and reducing the
burden on FL participants. It promotes collaborative FM development,
democratizing the process and fostering inclusivity and innovation. On the
other hand, FM, with its enormous size, pre-trained knowledge, and exceptional
performance, serves as a robust starting point for FL, facilitating faster
convergence and better performance under non-iid data. Additionally, leveraging
FM to generate synthetic data enriches data diversity, reduces overfitting, and
preserves privacy. By examining the interplay between FL and FM, this paper
aims to deepen the understanding of their synergistic relationship,
highlighting the motivations, challenges, and future directions. Through an
exploration of the challenges faced by FL and FM individually and their
interconnections, we aim to inspire future research directions that can further
enhance both fields, driving advancements and propelling the development of
privacy-preserving and scalable AI systems
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures
and new section
- …