Dinei A. F. Florêncio

Improved spread spectrum: a new modulation technique for robust watermarking

This paper introduces a new watermarking modulation technique, which we call improved spread spectrum (ISS). When compared with traditional spread spectrum (SS), the signal does not act as a noise source, leading to significant gains. In some examples, performance improvements over SS are 20 dB in signal-to-noise ratio (SNR) or ten or more orders of magnitude in the error probability. The proposed method achieves roughly the same noise robustness gain as quantization index modulation (QIM) but without the amplitude scale sensitivity of QIM. Our proposed ISS is as robust in practice as traditional SS.

Speech dereverberation via maximum-kurtosis subband adaptive filtering

This paper presents an efficient algorithm for high-quality speech capture in applications such as hands-free teleconferencing or voice recording by personal computers. We process the microphone signals by a subband adaptive filtering structure using a modulated complex lapped transform (MCLT), in which the subband filters are adapted to maximize the kurtosis of the linear prediction (LP) residual of the reconstructed speech. In this way, we attain good solutions to the problem of blind speech dereverberation. Experimental results with actual data, as well as with artificially difficult reverberant situations, show very good performance, both in terms of a significant reduction of the perceived reverberation, as well as improvement in spectral fidelity.

Decision-based median filter using local signal statistics

Noise removal is important in many applications. When the noise has impulsive characteristics, linear techniques do not perform well, and median filter or its derivatives are often used. Although median-based filters preserve edges reasonably well, they tend to remove some of the finer details in the image. Switching schemes--where the filter is switched between two or more filters--have been proposed, but they usually lack a decision rule efficient enough to yield good results on different regions of the image. In this paper we present a strategy to overcome this problem. A decision rule based on the second order local statistics of the signal (within a window) is used to switch between the identity filter and a median filter. The results on a test image show an improvement of around 4 dB over the median filter alone, and 2 dB over other techniques.

Maximum Likelihood Sound Source Localization and Beamforming for Directional Microphone Arrays in Distributed Meetings

In distributed meeting applications, microphone arrays have been widely used to capture superior speech sound and perform speaker localization through sound source localization (SSL) and beamforming. This paper presents a unified maximum likelihood framework of these two techniques, and demonstrates how such a framework can be adapted to create efficient SSL and beamforming algorithms for reverberant rooms and unknown directional patterns of microphones. The proposed method is closely related to steered response power-based algorithms, which are known to work extremely well in real-world environments. We demonstrate the effectiveness of the proposed method on challenging synthetic and real-world datasets, including over six hours of recorded meetings.

Nobody Sells Gold for the Price of Silver: Dishonesty, Uncertainty and the Underground Economy

The underground economy has attracted a lot of attention recently as a key component of cybercrime. In particular the IRC markets for stolen identities, phishing kits, botnets, and cybercrime related services have been extensively studied. It is suggested that sophisticated underground markets show great specialization and maturity. There are complex divisions of labor and service offerings for every need. Stolen credentials are traded in bulk for pennies on the dollar. It is suggested that large sums move on these markets.

Where do security policies come from

We examine the password policies of 75 different websites. Our goal is understand the enormous diversity of requirements: some will accept simple six-character passwords, while others impose rules of great complexity on their users. We compare different features of the sites to find which characteristics are correlated with stronger policies. Our results are surprising: greater security demands do not appear to be a factor. The size of the site, the number of users, the value of the assets protected and the frequency of attacks show no correlation with strength. In fact we find the reverse: some of the largest, most attacked sites with greatest assets allow relatively weak passwords. Instead, we find that those sites that accept advertising, purchase sponsored links and where the user has a choice show strong inverse correlation with strength. We conclude that the sites with the most restrictive password policies do not have greater security concerns, they are simply better insulated from the consequences of poor usability. Online retailers and sites that sell advertising must compete vigorously for users and traffic. In contrast to government and university sites, poor usability is a luxury they cannot afford. This in turn suggests that much of the extra strength demanded by the more restrictive policies is superfluous: it causes considerable inconvenience for negligible security improvement.

CROWDMOS: An approach for crowdsourcing mean opinion score studies

MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical significance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this paper, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.

Analyzing the Optimality of Predictive Transform Coding Using Graph-Based Models

In this letter, we provide a theoretical analysis of optimal predictive transform coding based on the Gaussian Markov random field (GMRF) model. It is shown that the eigen-analysis of the precision matrix of the GMRF model is optimal in decorrelating the signal. The resulting graph transform degenerates to the well-known 2-D discrete cosine transform (DCT) for a particular 2-D first order GMRF, although it is not a unique optimal solution. Furthermore, we present an optimal scheme to perform predictive transform coding based on conditional probabilities of a GMRF model. Such an analysis can be applied to both motion prediction and intra-frame predictive coding, and may lead to improvements in coding efficiency in the future.

Why does PHAT work well in lownoise, reverberative environments?

Among many existing time difference of arrival (TDOA) based sound source localization (SSL) algorithms, the phase transform (PHAT) is extremely popular for its excellent performance in low noise environments, even under relatively heavy reverberation. However, PHAT was developed as a heuristic approach and its working principle has not been completely understood. In this paper, we present the relationship between PHAT and a maximum likelihood (ML) framework for multi-microphone sound source localization. We show that when the environment noise approaches zero, PHAT is indeed a special case of the ML algorithm, which explains its good performance under low noise environments. In addition, we show that as long as the noise stays low, PHAT remains optimal in ML sense even when the room reverberation is heavy, which explains its robustness over reverberation.

Crowdsourcing subjective image quality evaluation

Subjective tests are generally regarded as the most reliable and definitive methods for assessing image quality. Nevertheless, laboratory studies are time consuming and expensive. Thus, researchers often choose to run informal studies or use objective quality measures, producing results which may not correlate well with human perception. In this paper we propose a cost-effective and convenient subjective quality measure called crowdMOS, obtained by having internet workers participate in MOS (mean opinion score) subjective quality studies. Since these workers cannot be supervised, we propose methods for detecting and discarding inaccurate or malicious scores. To facilitate this process, we offer an open source set of tools for Amazon Mechanical Turk, which is an internet marketplace for crowdsourcing. These tools completely automate the test design, score retrieval and statistical analysis, abstracting away the technical details of Mechanical Turk and ensuring a user-friendly, affordable and consistent test methodology. We demonstrate crowdMOS using data from the LIVE subjective quality image dataset, showing that it delivers accurate and repeatable results.