130 research outputs found

    On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems

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    Speaker verification systems have been widely used in smart phones and Internet of things devices to identify legitimate users. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effectively against speaker verification systems. The goal of this paper is to design a detector that can distinguish an original audio from an audio contaminated by adversarial attacks. Specifically, our designed detector, called MEH-FEST, calculates the minimum energy in high frequencies from the short-time Fourier transform of an audio and uses it as a detection metric. Through both analysis and experiments, we show that our proposed detector is easy to implement, fast to process an input audio, and effective in determining whether an audio is corrupted by FAKEBOB attacks. The experimental results indicate that the detector is extremely effective: with near zero false positive and false negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM) and i-vector speaker verification systems. Moreover, adaptive adversarial attacks against our proposed detector and their countermeasures are discussed and studied, showing the game between attackers and defenders

    Internet Epidemics: Attacks, Detection and Defenses, and Trends

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    Compression and denoising of time-resolved light transport

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    Exploiting temporal information of light propagation captured at ultra-fast frame rates has enabled applications such as reconstruction of complex hidden geometry and vision through scattering media. However, these applications require high-dimensional and high-resolution transport data, which introduces significant performance and storage constraints. Additionally, due to different sources of noise in both captured and synthesized data, the signal becomes significantly degraded over time, compromising the quality of the results. In this work, we tackle these issues by proposing a method that extracts meaningful sets of features to accurately represent time-resolved light transport data. Our method reduces the size of time-resolved transport data up to a factor of 32, while significantly mitigating variance in both temporal and spatial dimensions

    Effects of Different Shading Rates on the Photosynthesis and Corm Weight of Konjac Plant

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    To study the effects of shading level on the photosynthesis and corm weight of konjac plant, the chlorophyll fluorescence parameters, daily variation of relative electron transport rate (rETR), net photosynthetic rate (Pn), and corm weight of konjac plants under different treatments were measured and comparatively analyzed through covered cultivation of biennial seed corms with shade nets at different shading rates (0%, 50%, 70%, and 90%). The results showed that with the increase in shading rate, the maximum photochemical efficiency, potential activity, and non-photochemical quenching of photosystem â…ˇ (PSâ…ˇ) of konjac leaves constantly increased, whereas the actual photosynthetic efficiency, rETR, and photochemical quenching of PSâ…ˇ initially increased and then decreased. This result indicated that moderate shading could enhance the photosynthetic efficiency of konjac leaves. The daily variation of rETR in konjac plants under unshaded treatment showed a bimodal curve, whereas that under shaded treatment displayed a unimodal curve. The rETR of plants with 50% treatment and 70% treatment was gradually higher than that under unshaded treatment around noon. The moderate shading could increase the Pn of konjac leaves. The stomatal conductance and transpiration rate of the leaves under shaded treatment were significantly higher than those of the leaves under unshaded treatment. Shading could promote the growth of plants and increase corm weight. The comprehensive comparison shows that the konjac plants had strong photosynthetic capacity and high yield when the shading rate was 50%-70% for the area

    Universal Murray's law for optimised fluid transport in synthetic structures

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    Materials following Murray's law are of significant interest due to their unique porous structure and optimal mass transfer ability. However, it is challenging to construct such biomimetic hierarchical channels with perfectly cylindrical pores in synthetic systems following the existing theory. Achieving superior mass transport capacity revealed by Murray's law in nanostructured materials has thus far remained out of reach. We propose a Universal Murray's law applicable to a wide range of hierarchical structures, shapes and generalised transfer processes. We experimentally demonstrate optimal flow of various fluids in hierarchically planar and tubular graphene aerogel structures to validate the proposed law. By adjusting the macroscopic pores in such aerogel-based gas sensors, we also show a significantly improved sensor response dynamic. Our work provides a solid framework for designing synthetic Murray materials with arbitrarily shaped channels for superior mass transfer capabilities, with future implications in catalysis, sensing and energy applications.Comment: 19 pages, 4 figure

    xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

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    Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure
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