Existing DeepFake detection techniques primarily focus on facial manipulations, such as face-swapping or lip-syncing. However, advancements in text-to-video (T2V) and image-to-video (I2V) generative models now allow fully AI-generated synthetic content and seamless background alterations, challenging face-centric detection methods and demanding more versatile approaches.To address this, we introduce the Universal Network for Identifying Tampered and synthEtic videos (UNITE) model, which, unlike traditional detectors, captures full-frame manipulations. UNITE extends detection capabilities to scenarios without faces, non-human subjects, and complex background modifications. It leverages a transformer-based architecture that processes domain-agnostic features extracted from videos via the SigLIP-So400M foundation model. Given limited datasets encompassing both facial/background alterations and T2V/I2V content, we integrate task-irrelevant data alongside standard DeepFake datasets in training. We further mitigate the model’s tendency to over-focus on faces by incorporating an attention-diversity (AD) loss, which promotes diverse spatial attention across video frames. Combining AD loss with cross-entropy improves detection performance across varied contexts. Comparative evaluations demonstrate that UNITE outperforms state-of-the-art detectors on datasets featuring face/background manipulations and fully synthetic T2V/I2V videos, showcasing its adaptability and generalizable detection capabilities.

Users are often frustrated when they cannot view video links shared via blogs, social networks, and shared bookmark sites on their devices or suffer performance and usability problems when doing so. While other versions of the same content better suited to their device and network constraints may be available on other third-party hosting sites, these remain unusable because users cannot efficiently discover these and verify that these variants match the content publisher's original intent. Our vision is to enable consumers to leverage verifiable alternatives from different hosting sites that are best suited to their constraints to deliver a high quality of experience and enable content publishers to reach a wide audience with diverse operating conditions with minimal upfront costs. To this end, we make a case for information-bound references or IBRs that bind references to video content to the underlying information that a publisher wants to convey, decoupled from details such as protocols, hosts, file names, or the underlying bits. This paper addresses key challenges in the design and implementation of IBR generation and resolution mechanisms, and presents an evaluation of the benefits IBRs offer.

Recent studies have shown that web browsing is one of the most prominent cellular applications. It is therefore important for cellular network operators to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' web browsing Quality-of-Experience (web QoE). Understanding the relationship between web QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact web QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure web QoE metrics, such as abandonment rate and session length. In this paper, we first devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. We then present a large-scale study characterizing the impact of network characteristics on web QoE using a month-long anonymized dataset collected from a major cellular network provider. Our results show that improving signal-to-noise ratio, decreasing load and reducing handovers can improve user experience. We find that web QoE is very sensitive to inter-radio-access-technology (IRAT) handovers. We further find that higher radio data link rate does not necessarily lead to better web QoE. Since many network characteristics are interrelated, we also use machine learning to accurately model the influence of radio network characteristics on user experience metrics. This model can be used by cellular network operators to prioritize the improvement of network factors that most influence web QoE.

The rapid growth of fast analytics systems, that require data processing in memory, makes memory capacity an increasingly-precious resource. This paper introduces a new compressed data structure called a Compressed Buffer Tree (CBT). Using a combination of techniques including buffering, compression, and serialization, CBTs improve the memory efficiency and performance of the GroupBy-Aggregate abstraction that forms the basis of not only batch-processing models like MapReduce, but recent fast analytics systems too. For streaming workloads, aggregation using the CBT uses 21--42% less memory than using Google SparseHash with up to 16% better throughput. The CBT is also compared to batch-mode aggregators in MapReduce runtimes such as Phoenix++ and Metis and consumes 4x and 5x less memory with 1.5--2x and 3--4x more performance respectively.

Users are often frustrated when they cannot view video links shared via blogs, social networks, and shared bookmark sites on their devices or suffer performance and usability problems when doing so. While other versions of the same content better suited to their device and network constraints may be available on other third-party hosting sites, these remain unusable because users cannot efficiently discover these and verify that these variants match the content publisher's original intent. Our vision is to enable consumers to leverage verifiable alternatives from different hosting sites that are best suited to their constraints to deliver a high quality of experience and enable content publishers to reach a wide audience with diverse operating conditions with minimal upfront costs. To this end, we make a case for information-bound references or IBRs that bind references to video content to the underlying information that a publisher wants to convey, decoupled from details such as protocols, hosts, file names, or the underlying bits. This paper addresses key challenges in the design and implementation of IBR generation and resolution mechanisms, and presents an evaluation of the benefits IBRs offer.

Video viewership over the Internet is rising rapidly, and market predictions suggest that video will comprise over 90\% of Internet traffic in the next few years. At the same time, there have been signs that the Content Delivery Network (CDN) infrastructure is being stressed by ever-increasing amounts of video traffic. To meet these growing demands, the CDN infrastructure must be designed, provisioned and managed appropriately. Federated telco-CDNs and hybrid P2P-CDNs are two content delivery infrastructure designs that have gained significant industry attention recently. We observed several user access patterns that have important implications to these two designs in our unique dataset consisting of 30 million video sessions spanning around two months of video viewership from two large Internet video providers. These include partial interest in content, regional interests, temporal shift in peak load and patterns in evolution of interest. We analyze the impact of our findings on these two designs by performing a large scale measurement study. Surprisingly, we find significant amount of synchronous viewing behavior for Video On Demand (VOD) content, which makes hybrid P2P-CDN approach feasible for VOD and suggest new strategies for CDNs to reduce their infrastructure costs. We also find that federation can significantly reduce telco-CDN provisioning costs by as much as 95%.

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20\% improvement in overall user engagement compared to strawman approaches.

Over the past few years video viewership over the Internet has risen dramatically and market predictions suggest that video will account for more than 50% of the traffic over the Internet in the next few years. Unfortunately, there has been signs that the Content Delivery Network (CDN) infrastructure is being stressed with the increasing video viewership load. Our goal in this paper is to provide a first step towards a principled understanding of how the content delivery infrastructure must be designed and provisioned to handle the increasing workload by analyzing video viewing behaviors and patterns in the wild. We analyze various viewing behaviors using a dataset consisting of over 30 million video sessions spanning two months of viewership from two large Internet video providers. In these preliminary results, we observe viewing patterns that have significant impact on the design of the video delivery infrastructure.

An imminent challenge that content providers, CDNs, third-party analytics and optimization services, and video player designers in the Internet video ecosystem face is the lack of a single "gold standard" to evaluate different competing solutions. Existing techniques that describe the quality of the encoded signal or controlled studies to measure opinion scores do not translate directly into user experience at scale. Recent work shows that measurable performance metrics such as buffering, startup time, bitrate, and number of bitrate switches impact user experience. However, converting these observations into a quantitative quality-of-experience metric turns out to be challenging since these metrics are interrelated in complex and sometimes counter-intuitive ways, and their relationship to user experience can be unpredictable. To further complicate things, many confounding factors are introduced by the nature of the content itself (e.g., user interest, genre). We believe that the issue of interdependency can be addressed by casting this as a machine learning problem to build a suitable predictive model from empirical observations. We also show that setting up the problem based on domain-specific and measurement-driven insights can minimize the impact of the various confounding factors to improve the prediction performance.

This paper presents the design of a novel architecture called CARE (Content-Aware Redundancy Elimination) that enables maximizing the informational value that challenged networks offer their users. We focus on emerging applications for situational awareness in disaster affected regions. Motivated by advances in computer vision algorithms, we propose to incorporate image similarity detection algorithms in the forwarding path of these networks. The purpose is to handle the large generation of redundant content. We outline the many issues involved in such a vision. With a Delay-Tolerant Network (DTN) setup, our simulations demonstrate that CARE can substantially boost the number of unique messages that escape the disaster zone, and it can also deliver them faster. These benefits are achieved despite the energy overhead needed by the similarity detectors.

Multimedia content is a dominant fraction of Internet usage today. At the same time, there is significant heterogeneity in video presentation modes and operating conditions of Internet-enabled devices that access such content. Users are often interested in the content, rather than the specific sources or the formats. The host-centric format of the current Internet does not support these requirements naturally. Neither do the recent data-centric naming proposals, since they rely on naming content based on raw byte-level hashing schemes. We argue that to meet these requirements, enabling content retrieval mechanisms to name and query directly for the underlying information is a good way forward. In addition to decoupling content from available sources and transfer protocols, these "information-aware names" or InfoNames explicitly decouple the information from content presentation factors as well. We envision an InfoName Resolution System (IRS) to resolve location based on InfoNames, while taking into account the operating conditions of devices. In this demo, we present an application to show how InfoNames can serve as presentation-invariant and portable names to fetch video content independent of device capabilities and resource constraints.

Careful Channel Assignment (CA) and Link Scheduling (LS), tuned to the traffic demand in the network, are required to efficiently utilize Multi-Channel Multi-Radio (MC-MR) Wireless Mesh Networks (WMNs). In a dynamic network, where the traffic demand keeps changing with time, we need to reconfigure the CA and LS to suit to the changing demands. But, a change in CA leads to disruption of traffic resulting in a less reliable and lossy network. In this paper, we propose a theoretical framework to evaluate the efficiency of channel reconfiguration by taking into consideration the two conflicting objectives of maximizing network throughput and minimizing the reconfiguration overhead. A channel reconfiguration scheme that takes into account the current state of the network can find a new CA with significantly less overhead caused by reconfiguration. We propose and evaluate polynomially bounded heuristic algorithms for performing demand-based and state aware channel reconfiguration. Further, in a highly dynamic network scenario, performing reconfiguration very frequently to suit every traffic demand will lead to high reconfiguration overhead. Whereas doing it less frequently will lead to underutilization of the network. Hence, we propose a scheme that employs prediction techniques to estimate the future traffic demands in order to reduce the frequency of reconfiguration considering the long term traffic demand and conduct simulation studies to evaluate this scheme.