3,555 research outputs found
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain
We present ARCHANGEL; a novel distributed ledger based system for assuring
the long-term integrity of digital video archives. First, we describe a novel
deep network architecture for computing compact temporal content hashes (TCHs)
from audio-visual streams with durations of minutes or hours. Our TCHs are
sensitive to accidental or malicious content modification (tampering) but
invariant to the codec used to encode the video. This is necessary due to the
curatorial requirement for archives to format shift video over time to ensure
future accessibility. Second, we describe how the TCHs (and the models used to
derive them) are secured via a proof-of-authority blockchain distributed across
multiple independent archives. We report on the efficacy of ARCHANGEL within
the context of a trial deployment in which the national government archives of
the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201
Twofold Video Hashing with Automatic Synchronization
Video hashing finds a wide array of applications in content authentication,
robust retrieval and anti-piracy search. While much of the existing research
has focused on extracting robust and secure content descriptors, a significant
open challenge still remains: Most existing video hashing methods are fallible
to temporal desynchronization. That is, when the query video results by
deleting or inserting some frames from the reference video, most existing
methods assume the positions of the deleted (or inserted) frames are either
perfectly known or reliably estimated. This assumption may be okay under
typical transcoding and frame-rate changes but is highly inappropriate in
adversarial scenarios such as anti-piracy video search. For example, an illegal
uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion
etc by performing a cleverly designed non-uniform resampling of the video. We
present a new solution based on dynamic time warping (DTW), which can implement
automatic synchronization and can be used together with existing video hashing
methods. The second contribution of this paper is to propose a new robust
feature extraction method called flow hashing (FH), based on frame averaging
and optical flow descriptors. Finally, a fusion mechanism called distance
boosting is proposed to combine the information extracted by DTW and FH.
Experiments on real video collections show that such a hash extraction and
comparison enables unprecedented robustness under both spatial and temporal
attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International
Conference o
Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics
This paper proposes an efficient bitwise solution to the single-channel
source separation task. Most dictionary-based source separation algorithms rely
on iterative update rules during the run time, which becomes computationally
costly especially when we employ an overcomplete dictionary and sparse encoding
that tend to give better separation results. To avoid such cost we propose a
bitwise scheme on hashed spectra that leads to an efficient posterior
probability calculation. For each source, the algorithm uses a partial rank
order metric to extract robust features that form a binarized dictionary of
hashed spectra. Then, for a mixture spectrum, its hash code is compared with
each source's hashed dictionary in one pass. This simple voting-based
dictionary search allows a fast and iteration-free estimation of ratio masking
at each bin of a signal spectrogram. We verify that the proposed BitWise Source
Separation (BWSS) algorithm produces sensible source separation results for the
single-channel speech denoising task, with 6-8 dB mean SDR. To our knowledge,
this is the first dictionary based algorithm for this task that is completely
iteration-free in both training and testing
Panako: a scalable acoustic fingerprinting system handling time-scale and pitch modification
In this paper a scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented. The aim of acoustic fingerprinting is to identify identical, or recognize similar, audio fragments in a large set using condensed representations of audio signals, i.e. fingerprints. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The new system, presented here, handles a variety of distortions well. It is designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable. After a query, the system returns the start time in the reference audio, and the amount of pitch shift and tempo change that has been applied. The design of the system that offers this unique combination of features is the main contribution of this research. The fingerprint itself consists of a combination of key points in a Constant-Q spectrogram. The system is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30.000 songs. The results show that the system responds quickly and reliably on queries, while handling time and pitch scale modifications of up to ten percent
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
We address the problem of cross-modal information retrieval in the domain of
remote sensing. In particular, we are interested in two application scenarios:
i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery,
and ii) multi-label image retrieval between very high resolution (VHR) images
and speech based label annotations. Notice that these multi-modal retrieval
scenarios are more challenging than the traditional uni-modal retrieval
approaches given the inherent differences in distributions between the
modalities. However, with the growing availability of multi-source remote
sensing data and the scarcity of enough semantic annotations, the task of
multi-modal retrieval has recently become extremely important. In this regard,
we propose a novel deep neural network based architecture which is considered
to learn a discriminative shared feature space for all the input modalities,
suitable for semantically coherent information retrieval. Extensive experiments
are carried out on the benchmark large-scale PAN - multi-spectral DSRSID
dataset and the multi-label UC-Merced dataset. Together with the Merced
dataset, we generate a corpus of speech signals corresponding to the labels.
Superior performance with respect to the current state-of-the-art is observed
in all the cases
Optimization of star research algorithm for esmo star tracker
This paper explains in detail the design and the development of a software research star algorithm, embedded on a star tracker, by the ISAE/SUPAERO team. This research algorithm is inspired by musical techniques. This work will be carried out as part of the ESMO (European Student Moon Orbiter) project by different teams of students and professors from ISAE/SUPAERO (Institut Supe ́rieur de l’Ae ́ronautique et de l’Espace). Till today, the system engineering studies have been completed and the work that will be presented will concern the algorithmic and the embedded software development. The physical architecture of the sensor relies on APS 750 developed by the CIMI laboratory of ISAE/SUPAERO. First, a star research algorithm based on the image acquired in lost-in-space mode (one of the star tracker opera- tional modes) will be presented; it is inspired by techniques of musical recognition with the help of the correlation of digital signature (hash) with those stored in databases. The musical recognition principle is based on finger- printing, i.e. the extraction of points of interest in the studied signal. In the musical context, the signal spectrogram is used to identify these points. Applying this technique in image processing domain requires an equivalent tool to spectrogram. Those points of interest create a hash and are used to efficiently search within the database pre- viously sorted in order to be compared. The main goals of this research algorithm are to minimise the number of steps in the computations in order to deliver information at a higher frequency and to increase the computation robustness against the different possible disturbances
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