16 research outputs found

    Transparent encryption with scalable video communication: Lower-latency, CABAC-based schemes

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    Selective encryption masks all of the content without completely hiding it, as full encryption would do at a cost in encryption delay and increased bandwidth. Many commercial applications of video encryption do not even require selective encryption, because greater utility can be gained from transparent encryption, i.e. allowing prospective viewers to glimpse a reduced quality version of the content as a taster. Our lightweight selective encryption scheme when applied to scalable video coding is well suited to transparent encryption. The paper illustrates the gains in reducing delay and increased distortion arising from a transparent encryption that leaves reduced quality base layer in the clear. Reduced encryption of B-frames is a further step beyond transparent encryption in which the computational overhead reduction is traded against content security and limited distortion. This spectrum of video encryption possibilities is analyzed in this paper, though all of the schemes maintain decoder compatibility and add no bitrate overhead as a result of jointly encoding and encrypting the input video by virtue of carefully selecting the entropy coding parameters that are encrypted. The schemes are suitable both for H.264 and HEVC codecs, though demonstrated in the paper for H.264. Selected Content Adaptive Binary Arithmetic Coding (CABAC) parameters are encrypted by a lightweight Exclusive OR technique, which is chosen for practicality

    Confidentiality of a selectively encrypted H.264 coded video bit-stream

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    It is an assumption that selective encryption does not strongly protect confidentiality owing to the partial visibility of some video data. This is because, though encryption keys may be difficult to derive, an enhanced version of selectively encrypted video sequence might be found from knowledge of the unencrypted parts of the sequence. An efficient selective encryption method for syntax elements of H.264 encoded video was recently proposed at the entropy coding stage of an H.264 encoder. Using this recent scheme as an example, the purpose of this paper is a comprehensive cryptanalysis of selectively encrypted H.264 bit-streams to contradict the previous assumption that selective encryption is vulnerable. The novel cryptanalysis methods presented in this paper analyze the ability of an attacker to improve the quality of the encrypted video stream to make it watchable. The conclusion is drawn that if the syntax elements for selective encryption are chosen using statistical and structural characteristics of the video, then the selective encryption method is secure. The cryptanalysis is performed by taking into account the probability distribution of syntax elements within the video sequence, the relationship of syntax elements with linear regression analysis and the probability of successfully attacking them in order to enhance the visual quality. The results demonstrate the preservation of distorted video quality even after considering many possible attacks on: the whole video sequence; each video frame; and on small video segments known as slices. © 2014 Elsevier Inc. All rights reserved

    Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

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    Interoperable conditional access with video selective encryption for portable devices

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    The pay-TV industry seeks to extend its reach to portable display devices. At the same time, it seeks to ensure a horizontal market by making interoperable the Conditional Access Systems (CASs) employed to protect content. To achieve interoperability for such devices, this paper proposes a form of selective encryption for video that allows simultaneous distribution of a small percentage of video data on a per-CAS basis, allowing sharing of the unencrypted video between the CASs. The bitrate overhead for each additional CAS enabled is found to be on average 7.41 %, whereas the computational overhead amounts to no more than 40 ms for the benchmark sequences tested. Adaptation of CAS to transparent encryption of scalable video is also demonstrated in this paper

    Sufficient encryption based on entropy coding syntax elements of H.264/SVC

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    While much attention has been paid to securing the transmission of single-layer video, multi-layer scalable video also deserves consideration. This paper presents a sufficient encryption (SE) scheme for the H.264 Scalable Video Coding (SVC) extension that maintains the compression efficiency and the decoder format compliancy of the bitstream, without compromising its confidentiality. SE is achieved by applying encryption of carefully selected codewords or bin-strings of the Context-Adaptive Variable-Length Coding (CAVLC) and Context-Adaptive Binary Arithmetic Coding (CABAC) entropy coders respectively. The selection of exactly what to encrypt is what distinguishes this contribution from that of others. The performance of the scheme is tested on sequences with varying spatial resolutions, thus demonstrating the advantages of the scheme when compared to alternative techniques. These advantages include: minimal computational delay by encrypting partial data; no bit-rate escalation by keeping the compression ratio unchanged; and format compliancy of the bit-stream at the decoder. The detailed security and comparative evaluation of the scheme confirms that it is suitable for commercial, real-time applications. As there is a minimal increase in processing requirements, the scheme is highly suitable for video distribution to users who have subscribed to differing video qualities on end systems ranging from small handheld devices to those capable of high spatial resolutions and frame rates

    Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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    Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation
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