A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing

With the widespread adoption of edge computing technologies and the increasing prevalence of deep learning models in these environments, the security risks and privacy threats to models and data have grown more acute. Attackers can exploit various techniques to illegally obtain models or misuse data, leading to serious issues such as intellectual property infringement and privacy breaches. Existing model access control technologies primarily rely on traditional encryption and authentication methods; however, these approaches exhibit significant limitations in terms of flexibility and adaptability in dynamic environments. Although there have been advancements in model watermarking techniques for marking model ownership, they remain limited in their ability to proactively protect intellectual property and prevent unauthorized access. To address these challenges, we propose a novel model access control method tailored for edge computing environments. This method leverages image style as a licensing mechanism, embedding style recognition into the model's operational framework to enable intrinsic access control. Consequently, models deployed on edge platforms are designed to correctly infer only on license data with specific style, rendering them ineffective on any other data. By restricting the input data to the edge model, this approach not only prevents attackers from gaining unauthorized access to the model but also enhances the privacy of data on terminal devices. We conducted extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and FACESCRUB, and the results demonstrate that our method effectively prevents unauthorized access to the model while maintaining accuracy. Additionally, the model shows strong resistance against attacks such as forged licenses and fine-tuning. These results underscore the method's usability, security, and robustness.

Gaussian in the Wild: 3D Gaussian Splatting for Unconstrained Image Collections

Novel view synthesis from unconstrained in-the-wild images remains a meaningful but challenging task. The photometric variation and transient occluders in those unconstrained images make it difficult to reconstruct the original scene accurately. Previous approaches tackle the problem by introducing a global appearance feature in Neural Radiance Fields (NeRF). However, in the real world, the unique appearance of each tiny point in a scene is determined by its independent intrinsic material attributes and the varying environmental impacts it receives. Inspired by this fact, we propose Gaussian in the wild (GS-W), a method that uses 3D Gaussian points to reconstruct the scene and introduces separated intrinsic and dynamic appearance feature for each point, capturing the unchanged scene appearance along with dynamic variation like illumination and weather. Additionally, an adaptive sampling strategy is presented to allow each Gaussian point to focus on the local and detailed information more effectively. We also reduce the impact of transient occluders using a 2D visibility map. More experiments have demonstrated better reconstruction quality and details of GS-W compared to previous methods, with a $1000\times$ increase in rendering speed.

Web3 Meets AI Marketplace: Exploring Opportunities, Analyzing Challenges, and Suggesting Solutions

Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention, emerging as a new field with the potential to address the limitations of each. In this article, we will focus on the integration of web3 and the AI marketplace, where AI services and products can be provided in a decentralized manner (DeAI). A comprehensive review is provided by summarizing the opportunities and challenges on this topic. Additionally, we offer analyses and solutions to address these challenges. We've developed a framework that lets users pay with any kind of cryptocurrency to get AI services. Additionally, they can also enjoy AI services for free on our platform by simply locking up their assets temporarily in the protocol. This unique approach is a first in the industry. Before this, offering free AI services in the web3 community wasn't possible. Our solution opens up exciting opportunities for the AI marketplace in the web3 space to grow and be widely adopted.

Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed AI Training

In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain technology. The PoT protocol utilizes the practical Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global states. To evaluate the performance of the protocol design, we present an implementation of a decentralized training network (DTN) that adopts the PoT protocol. Our results indicate that the protocol exhibits considerable potential in terms of task throughput, system robustness, and network security.

NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds

We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction to guide the learning of implicit neural representations of 3D indoor scenes. Concretely, an off-the-shelf neural reconstruction method is leveraged to generate a geometry scaffold. Then, two loss functions based on the holistic priors are proposed to improve the learning of NeRF: 1) A robust depth loss that can tolerate the error of the pseudo depth map to guide the geometry learning of NeRF; 2) A variance loss to regularize the variance of implicit neural representations to reduce the geometry and color ambiguity in the learning procedure. These two loss functions are modulated during NeRF optimization according to the view coverage information to reduce the negative influence brought by the view coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms state-of-the-art view synthesis methods quantitatively and qualitatively on indoor scenes, achieving high-fidelity free navigation results.