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A Comprehensive Symbolic Analysis of TLS 1.3
No full textThe TLS protocol is intended to enable secure end-to-end communication over insecure networks, including the Internet. Unfortunately, this goal has been thwarted a number of times throughout the protocol's tumultuous lifetime, resulting in the need for a new version of the protocol, namely TLS 1.3. Over the past three years, in an unprecedented joint design effort with the academic community, the TLS Working Group has been working tirelessly to enhance the security of TLS.
We further this effort by constructing the most comprehensive, faithful, and modular symbolic model of the TLS 1.3 draft 21 release candidate, and use the TAMARIN prover to verify the claimed TLS 1.3 security requirements, as laid out in draft 21 of the specification. In particular, our model covers all handshake modes of TLS 1.3.
Our analysis reveals an unexpected behaviour, which we expect will inhibit strong authentication guarantees in some implementations of the protocol. In contrast to previous models, we provide a novel way of making the relation between the TLS specification and our model explicit: we provide a fully annotated version of the specification that clarifies what protocol elements we modelled, and precisely how we modelled these elements. We anticipate this model artifact to be of great benefit to the academic community and the TLS Working Group alike
Movie Description
No full textAudio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015
