Search for: All records

360 video streaming presents unique challenges in bandwidth efficiency and motion-to-photon (MTP) latency, particularly for live multi-user scenarios. While viewport prediction (VP) has emerged as the dominant solution, its effectiveness in live streaming is limited by training data scarcity and the unpredictability of live content. We present 360LIVECAST, the first practical multicast framework for live 360 video that eliminates the need for VP through two key innovations: (1) a novel viewport hull representation that combines current viewports with marginal regions, enabling local frame synthesis while reducing bandwidth by 60% compared to full panorama transmission, and (2) an viewport-specific hierarchical multicast framework leveraging edge computing to handle viewer dynamics while maintaining sub-25ms MTP latency. Extensive evaluation using real-world network traces and viewing trajectories demonstrates that 360LIVECAST achieves 26.9% lower latency than VP-based approaches while maintaining superior scalability. 

Efficient single instance segmentation is critical for unlocking features in on-the-fly mobile imaging applications, such as photo capture and editing. Existing mobile solutions often restrict segmentation to portraits or salient objects due to computational constraints. Recent advancements like the Segment Anything Model improve accuracy but remain computationally expensive for mobile, because it processes the entire image with heavy transformer backbones. To address this, we propose TraceNet, a one-click-driven single instance segmentation model. TraceNet segments a user-specified instance by back-tracing the receptive field of a ConvNet backbone, focusing computations on relevant regions and reducing inference cost and memory usage during mobile inference. Starting from user needs in real mobile applications, we define efficient single-instance segmentation tasks and introduce two novel metrics to evaluate both accuracy and robustness to low-quality input clicks. Extensive evaluations on the MS-COCO and LVIS datasets highlight TraceNet’s ability to generate high-quality instance masks efficiently and accurately while demonstrating robustness to imperfect user inputs. 

Modern vehicles are largely controlled by many embedded computers, known as Electronic Control Units (ECUs). The increased use of ECUs has brought many in-vehicle security concerns. Specifically, injection of malware into ECUs poses a significant risk to vehicle operation. Indeed, many ECU malware injection attacks have been performed, and much work has been introduced towards mitigating these vulnerabilities. A main defense is for ECUs to perform a self-attestation over their firmware state. However, most current self-attestation solutions do not enable runtime checking due to their high computational cost. Additionally, existing solutions mostly do not incorporate any ECU self-repairing in coordination with the attestation mechanisms. In this work, we have designed FSAVER, a highly efficient self-attestation and self-repair framework for in-vehicle ECUs. For the self-attestation, we adapt highly efficient spot-checking techniques, so that the firmware can be checked periodically at runtime. To perform these attestations, we rely on the TEE already equipped within each ECU. For self-repair, we take advantage of the isolated flash memory controller (FMC) in the storage device. Specifically, we coordinate it with the update mechanism and self-attestations to guarantee that the latest benign firmware version can always be restored. To realize this while malware is running, a special mechanism has been carefully developed to notify the FMC of the malicious presence. 

Neural Radiance Field (NeRF) has emerged as a powerful technique for 3D scene representation due to its high rendering quality. Among its applications, mobile NeRF video-on-demand (VoD) is especially promising, beneting from both the scalability of the mobile devices and the immersive experience oered by NeRF. However, streaming NeRF videos over real-world networks presents signi cant challenges, particularly due to limited bandwidth and temporal dynamics. To address these challenges, we propose NeRFlow, a novel framework that enables adaptive streaming for NeRF videos through both bitrate and viewpoint adaptation. NeRFlow solves three fundamental problems: rst, it employs a rendering-adaptive pruning technique to determine voxel importance, selectively reducing data size without sacricing rendering quality. Second, it introduces a viewpoint-aware adaptation module that eciently compensates for uncovered regions in real time by combining preencoded master and sub-frames. Third, it incorporates a QoE-aware bitrate ladder generation framework, leveraging a genetic algorithm to optimize the number and conguration of bitrates while accounting for bandwidth dynamics and ABR algorithms. Through extensive experiments, NeRFlow is demonstrated to eectively improve user Quality of Experience (QoE) by 31.3% to 41.2%, making it an ecient solution for NeRF video streaming. 

Abstract Ransomware attacks are increasingly prevalent in recent years. Crypto-ransomware corrupts files on an infected device and demands a ransom to recover them. In computing devices using flash memory storage (e.g., SSD, MicroSD, etc.), existing designs recover the compromised data by extracting the entire raw flash memory image, restoring the entire external storage to a good prior state. This is feasible through taking advantage of the out-of-place updates feature implemented in the flash translation layer (FTL). However, due to the lack of “file” semantics in the FTL, such a solution does not allow a fine-grained data recovery in terms of files. Considering the file-centric nature of ransomware attacks, recovering the entire disk is mostly unnecessary. In particular, the user may just wish a speedy recovery of certain critical files after a ransomware attack. In this work, we have designed$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$, a new ransomware defense strategy that can support fine-grained per file data recovery after the ransomware attack. Our key idea is that, to restore a file corrupted by the ransomware, we (1) restore its file system metadata via file system forensics, and (2) extract its file data via raw data extraction from the FTL, and (3) assemble the corresponding file system metadata and the file data. Another essential aspect of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$is that we add a garbage collection delay and freeze mechanism into the FTL so that no raw data will be lost prior to the recovery and, additionally, the raw data needed for the recovery can be always located. A prototype of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$has been developed and our experiments using real-world ransomware samples demonstrate the effectiveness of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$. We also demonstrate that$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$has negligible storage cost and performance impact. 

In the history of access control, nearly every system designed has relied on the operating system (OS) to enforce the access control protocols. However, if the OS (and specifically root access) is compromised, there are few if any solutions that can get users back into their system efficiently. In this work, we have proposed a novel approach that allows secure and efficient rollback of file access control after an adversary compromises the OS and corrupts the access control metadata. Our key observation is that the underlying flash memory typically performs out-of-place updates. Taking advantage of this unique feature, we can extract the “stale data” specific for OS access control, by performing low-level disk forensics over the raw flash memory. This allows efficiently rolling back the OS access control to a state pre-dating the compromise. To justify the feasibility of the proposed approach, we have implemented it in a computing device using file system EXT2/EXT3 and open-sourced flash memory firmware OpenNFM. We also evaluated the potential impact of our design on the original system. Experimental results indicate that the performance of the affected drive is not significantly impacted.