Multi-modal fingerprinting for multimedia assets: optimisation of media security protection in online distribution environments

Since the advent of the internet in every day life in the 1990s, the barriers to producing, distributing and consuming multimedia data such as videos, music, ebooks, etc. have steadily been lowered for most computer users so that almost everyone with internet access can join the online communities who both produce, consume and of course also share media artefacts. Along with this trend, the violation of personal data privacy and copyright has increased with illegal file sharing being rampant across many online communities particularly for certain music genres and amongst the younger age groups. This has had a devastating effect on the traditional media distribution market; in most cases leaving the distribution companies and the content owner with huge financial losses. To prove that a copyright violation has occurred one can deploy fingerprinting mechanisms to uniquely identify the property. However this is currently based on only uni-modal approaches. In this paper we describe some of the design challenges and architectural approaches to multi-modal fingerprinting currently being examined for evaluation studies within a PhD research programme on optimisation of multi-modal fingerprinting architectures. Accordingly we outline the available modalities that are being integrated through this research programme which aims to establish the optimal architecture for multi-modal media security protection over the internet as the online distribution environment for both legal and illegal distribution of media products.

Multi-level Input Fusion in multi-modal systems

Context-aware multimodal interactive systems aim to adapt to the needs and behavioural patterns of users and offer a way forward for enhancing the efficacy and quality of experience (QoE) in human-computer interaction. The various modalities that constribute to such systems each provide a specific uni-modal response that is integratively presented as a multi-modal interface capable of interpretation of multi-modal user input and appropriately responding to it through dynamically adapted multi-modal interactive flow management , This paper presents an initial background study in the context of the first phase of a PhD research programme in the area of optimisation of data fusion techniques to serve multimodal interactivite systems, their applications and requirements.

Cross-modal preference acquisition: evaluative conditioning of pictures by affective olfactory and auditory cues

In this research, a cross-model paradigm was chosen to test the hypothesis that affective olfactory and auditory cues paired with neutral visual stimuli bearing no resemblance or logical connection to the affective cues can evoke preference shifts in those stimuli. Neutral visual stimuli of abstract paintings were presented simultaneously with liked and disliked odours and sounds, with neutral-neutral pairings serving as controls. The results confirm previous findings that the affective evaluation of previously neutral visual stimuli shifts in the direction of contingently presented affective auditory stimuli. In addition, this research shows the presence of conditioning with affective odours having no logical connection with the pictures.

Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar-ethanol-oil nexus in Brazil

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices.

A framework towards multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but what amounts to its essence or ”DNA”. Current approaches show insufficient deployment of three types of knowledge that could be brought to bear in providing a finger printing framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Foci of Interest (FoI) in an image or cross media artefact. Thus our proposed framework aims to deliver selective composite fingerprinting that remains responsive to the requirements for protection of whole or parts of an image which may be of particularly interest and be especially vulnerable to attempts at rights violation. This is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals as well as the inevitably needed market intelligence knowledge such as customers’ social networks interests profiling which we can deploy as a crucial component of our Fingerprinting Collateral Knowledge. This is used in selecting the special FoIs within an image or other media content that have to be selectively and collaterally protected.

A framework towards a multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but instead what amounts to its essence or 'DNA'. Current approaches show insufficient deployment of various types of knowledge that could be brought to bear in providing a fingerprinting framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Zones of Interest (ZoI) in an image or cross media artefact. The proposed framework aims to deliver selective composite fingerprinting that is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals and also the inevitably needed market intelligence knowledge such as customers' social networks interests profiling which we can deploy as a crucial component of our fingerprinting collateral knowledge.