15,550 research outputs found
Improving random number generators by chaotic iterations. Application in data hiding
In this paper, a new pseudo-random number generator (PRNG) based on chaotic
iterations is proposed. This method also combines the digits of two XORshifts
PRNGs. The statistical properties of this new generator are improved: the
generated sequences can pass all the DieHARD statistical test suite. In
addition, this generator behaves chaotically, as defined by Devaney. This makes
our generator suitable for cryptographic applications. An illustration in the
field of data hiding is presented and the robustness of the obtained data
hiding algorithm against attacks is evaluated.Comment: 6 pages, 8 figures, In ICCASM 2010, Int. Conf. on Computer
Application and System Modeling, Taiyuan, China, pages ***--***, October 201
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected to a conventional ML detector for fast classification. If a
piece of software receives a borderline classification, it is subjected to
further analysis via more performance expensive and more accurate DL methods,
via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
to the execution of software subjected to deep learning analysis as a way to
"buy time" for DL analysis and to rate-limit the impact of possible malware in
the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
877 commonly used benign software samples from various categories for the
Windows OS. Our results show that the false positive rate for conventional ML
methods can reach 20%, and for modern DL methods it is usually below 6%.
However, the classification time for DL can be 100X longer than conventional ML
methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
percentage of software subjected to DL analysis was approximately 40% on
average. Further, the application of delays in software subjected to ML reduced
the detection time by approximately 10%. Finally, we found and discussed a
discrepancy between the detection accuracy offline (analysis after all traces
are collected) and on-the-fly (analysis in tandem with trace collection). Our
insights show that conventional ML and modern DL-based malware detectors in
isolation cannot meet the needs of efficient and effective malware detection:
high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure
Easy over Hard: A Case Study on Deep Learning
While deep learning is an exciting new technique, the benefits of this method
need to be assessed with respect to its computational cost. This is
particularly important for deep learning since these learners need hours (to
weeks) to train the model. Such long training time limits the ability of (a)~a
researcher to test the stability of their conclusion via repeated runs with
different random seeds; and (b)~other researchers to repeat, improve, or even
refute that original work.
For example, recently, deep learning was used to find which questions in the
Stack Overflow programmer discussion forum can be linked together. That deep
learning system took 14 hours to execute. We show here that applying a very
simple optimizer called DE to fine tune SVM, it can achieve similar (and
sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84
times faster hours than deep learning method.
We offer these results as a cautionary tale to the software analytics
community and suggest that not every new innovation should be applied without
critical analysis. If researchers deploy some new and expensive process, that
work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201
Spread spectrum-based video watermarking algorithms for copyright protection
Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can
now benefit from hardware and software which was considered state-of-the-art several years
ago. The advantages offered by the digital technologies are major but the same digital
technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly
possible and relatively easy, in spite of various forms of protection, but due to the analogue
environment, the subsequent copies had an inherent loss in quality. This was a natural way of
limiting the multiple copying of a video material. With digital technology, this barrier
disappears, being possible to make as many copies as desired, without any loss in quality
whatsoever. Digital watermarking is one of the best available tools for fighting this threat.
The aim of the present work was to develop a digital watermarking system compliant with the
recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark
can be inserted in either spatial domain or transform domain, this aspect was investigated and
led to the conclusion that wavelet transform is one of the best solutions available. Since
watermarking is not an easy task, especially considering the robustness under various attacks
several techniques were employed in order to increase the capacity/robustness of the system:
spread-spectrum and modulation techniques to cast the watermark, powerful error correction
to protect the mark, human visual models to insert a robust mark and to ensure its invisibility.
The combination of these methods led to a major improvement, but yet the system wasn't
robust to several important geometrical attacks. In order to achieve this last milestone, the
system uses two distinct watermarks: a spatial domain reference watermark and the main
watermark embedded in the wavelet domain. By using this reference watermark and techniques
specific to image registration, the system is able to determine the parameters of the attack and
revert it. Once the attack was reverted, the main watermark is recovered. The final result is a
high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen
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