pwc-archive/papers-with-abstracts huggingface dataset:
Is Audio Spoof Detection Robust to Laundering Attacks?
Authors: Hashim Ali, Surya Subramani, Shefali Sudhir, Raksha Varahamurthy, Hafiz Malik
Date: 2024-08-27 00:00:00
Tasks: Voice Cloning

Voice-cloning (VC) systems have seen an exceptional increase in the realism of synthesized speech in recent years. The high quality of synthesized speech and the availability of low-cost VC services have given rise to many potential abuses of this technology. Several detection methodologies have been proposed over the years that can detect voice spoofs with reasonably good accuracy. However, these methodologies are mostly evaluated on clean audio databases, such as ASVSpoof 2019. This paper evaluates SOTA Audio Spoof Detection approaches in the presence of laundering attacks. In that regard, a new laundering attack database, called the ASVSpoof Laundering Database, is created. This database is based on the ASVSpoof 2019 (LA) eval database comprising a total of 1388.22 hours of audio recordings. Seven SOTA audio spoof detection approaches are evaluated on this laundered database. The results indicate that SOTA systems perform poorly in the presence of aggressive laundering attacks, especially reverberation and additive noise attacks. This suggests the need for robust audio spoof detection.
Spoken-Term Discovery using Discrete Speech Units
Authors: Benjamin van Niekerk, Julian Zaïdi, Marc-André Carbonneau, Herman Kamper
Date: 2024-08-26 00:00:00

Discovering a lexicon from unlabeled audio is a longstanding challenge for zero-resource speech processing. One approach is to search for frequently occurring patterns in speech. We revisit this idea with DUSTED: Discrete Unit Spoken-TErm Discovery. Leveraging self-supervised models, we encode input audio into sequences of discrete units. Next, we find repeated patterns by searching for similar unit sub-sequences, inspired by alignment algorithms from bioinformatics. Since discretization discards speaker information, DUSTED finds better matches across speakers, improving the coverage and consistency of the discovered patterns. We demonstrate these improvements on the ZeroSpeech Challenge, achieving state-of-the-art results on the spoken-term discovery track. Finally, we analyze the duration distribution of the patterns, showing that our method finds longer word- or phrase-like terms.
Beyond MR Image Harmonization: Resolution Matters Too
Authors: Savannah P. Hays, Samuel W. Remedios, Lianrui Zuo, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Blake E. Dewey, Jerry L. Prince
Date: 2024-08-29 00:00:00
Tasks: Image Harmonization, Super-Resolution

Magnetic resonance (MR) imaging is commonly used in the clinical setting to non-invasively monitor the body. There exists a large variability in MR imaging due to differences in scanner hardware, software, and protocol design. Ideally, a processing algorithm should perform robustly to this variability, but that is not always the case in reality. This introduces a need for image harmonization to overcome issues of domain shift when performing downstream analysis such as segmentation. Most image harmonization models focus on acquisition parameters such as inversion time or repetition time, but they ignore an important aspect in MR imaging -- resolution. In this paper, we evaluate the impact of image resolution on harmonization using a pretrained harmonization algorithm. We simulate 2D acquisitions of various slice thicknesses and gaps from 3D acquired, 1mm3 isotropic MR images and demonstrate how the performance of a state-of-the-art image harmonization algorithm varies as resolution changes. We discuss the most ideal scenarios for image resolution including acquisition orientation when 3D imaging is not available, which is common for many clinical scanners. Our results show that harmonization on low-resolution images does not account for acquisition resolution and orientation variations. Super-resolution can be used to alleviate resolution variations but it is not always used. Our methodology can generalize to help evaluate the impact of image acquisition resolution for multiple tasks. Determining the limits of a pretrained algorithm is important when considering preprocessing steps and trust in the results.
Two Dimensional Magnetic Current Imaging Via L1-Curl Regularized Divergence Free Wavelet Reconstruction
Authors: Christopher Miller, Adrian Mariano, Sean Oliver, Jacob Lenz, Dmitro Martynowych
Date: 2024-08-29 00:00:00

The reconstruction of current distributions from samples of their induced magnetic field is a challenging problem due to multiple factors. First, the problem of reconstructing general three dimensional current distributions is ill-posed. Second, the current-to-field operator performs a low-pass filter that dampens high-spatial frequency information, so that even in situations where the inversion is formally possible, attempting to employ the formal inverse will result in solutions with unacceptable noise. Most contemporary methods for reconstructing current distributions in two dimensions are based on Fourier techniques and apply a low pass filter to the $B$-field data, which prevents excessive noise amplification during reconstruction at the cost of admitting blurring in the reconstructed solution. In this report, we present a method of current recovery based on penalizing the $L1$ norm of the curl of the current distribution. The utility of this method is based on the observation that in microelectronics settings, the conductivity is piecewise constant. We also reconstruct the current fields using a divergence-free wavelet basis. This has the advantage of automatically enforcing current continuity and halving the number of unknowns that must be solved for. Additionally, the curl operator can be computed exactly and analytically in this wavelet expansion, which simplifies the application of the $L1-\textrm{curl}$ regularizer. We demonstrate improved reconstruction quality relative to Fourier-based techniques on both simulated and laboratory-acquired magnetic field data.
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
Authors: Feng Gao, Xuepeng Jin, Xiaowei Zhou, Junyu Dong, Qian Du
Date: 2024-08-26 00:00:00
Tasks: Computational Efficiency, image-classification, Image Classification, Mamba, Remote Sensing Image Classification

In the field of multi-source remote sensing image classification, remarkable progress has been made by using Convolutional Neural Network (CNN) and Transformer. Recently, Mamba-based methods built upon the State Space Model (SSM) have shown great potential for long-range dependency modeling with linear complexity, but they have rarely been explored for multi-source remote sensing image classification tasks. To address this issue, we propose the Multi-Scale Feature Fusion Mamba (MSFMamba) network, a novel framework designed for the joint classification of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR)/Synthetic Aperture Radar (SAR) data. The MSFMamba network is composed of three key components: the Multi-Scale Spatial Mamba (MSpa-Mamba) block, the Spectral Mamba (Spe-Mamba) block, and the Fusion Mamba (Fus-Mamba) block. The MSpa-Mamba block employs a multi-scale strategy to reduce computational cost and alleviate feature redundancy in multiple scanning routes, ensuring efficient spatial feature modeling. The Spe-Mamba block focuses on spectral feature extraction, addressing the unique challenges of HSI data representation. Finally, the Fus-Mamba block bridges the heterogeneous gap between HSI and LiDAR/SAR data by extending the original Mamba architecture to accommodate dual inputs, enhancing cross-modal feature interactions and enabling seamless data fusion. Together, these components enable MSFMamba to effectively tackle the challenges of multi-source data classification, delivering improved performance with optimized computational efficiency. Comprehensive experiments on four real-world multi-source remote sensing datasets demonstrate the superiority of MSFMamba outperforms several state-of-the-art methods. The source codes of MSFMamba are publicly available at https://github.com/oucailab/MSFMamba.
Image Provenance Analysis via Graph Encoding with Vision Transformer
Authors: Keyang Zhang, Chenqi Kong, Shiqi Wang, Anderson Rocha, Haoliang Li
Date: 2024-08-26 00:00:00
Tasks: graph construction, Misinformation

Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms. Image provenance analysis is crucial in this context, as it identifies relevant images within a database and constructs a relationship graph by mining hidden manipulation and transformation cues, thereby providing concrete evidence chains. This paper introduces a novel end-to-end deep learning framework designed to explore the structural information of provenance graphs. Our proposed method distinguishes from previous approaches in two main ways. First, unlike earlier methods that rely on prior knowledge and have limited generalizability, our framework relies upon a patch attention mechanism to capture image provenance clues for local manipulations and global transformations, thereby enhancing graph construction performance. Second, while previous methods primarily focus on identifying tampering traces only between image pairs, they often overlook the hidden information embedded in the topology of the provenance graph. Our approach aligns the model training objectives with the final graph construction task, incorporating the overall structural information of the graph into the training process. We integrate graph structure information with the attention mechanism, enabling precise determination of the direction of transformation. Experimental results show the superiority of the proposed method over previous approaches, underscoring its effectiveness in addressing the challenges of image provenance analysis.
Rate-Distortion-Perception Controllable Joint Source-Channel Coding for High-Fidelity Generative Communications
Authors: Kailin Tan, Jincheng Dai, Zhenyu Liu, Sixian Wang, Xiaoqi Qin, Wenjun Xu, Kai Niu, Ping Zhang
Date: 2024-08-26 00:00:00

End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, existing methods primarily optimize the trade-off between bandwidth cost and objective distortion, often failing to deliver visually pleasing results aligned with human perception. In this paper, we propose a novel rate-distortion-perception (RDP) jointly optimized joint source-channel coding (JSCC) framework to enhance perception quality in human communications. Our RDP-JSCC framework integrates a flexible plug-in conditional Generative Adversarial Networks (GANs) to provide detailed and realistic image reconstructions at the receiver, overcoming the limitations of traditional rate-distortion optimized solutions that typically produce blurry or poorly textured images. Based on this framework, we introduce a distortion-perception controllable transmission (DPCT) model, which addresses the variation in the perception-distortion trade-off. DPCT uses a lightweight spatial realism embedding module (SREM) to condition the generator on a realism map, enabling the customization of appearance realism for each image region at the receiver from a single transmission. Furthermore, for scenarios with scarce bandwidth, we propose an interest-oriented content-controllable transmission (CCT) model. CCT prioritizes the transmission of regions that attract user attention and generates other regions from an instance label map, ensuring both content consistency and appearance realism for all regions while proportionally reducing channel bandwidth costs. Comprehensive experiments demonstrate the superiority of our RDP-optimized image transmission framework over state-of-the-art engineered image transmission systems and advanced perceptual methods.
Fast Edge-Aware Occlusion Detection in the Context of Multispectral Camera Arrays
Authors: Frank Sippel, Jürgen Seiler, André Kaup
Date: 2024-08-26 00:00:00
Tasks: Disparity Estimation, Image Registration, SSIM

Multispectral imaging is very beneficial in diverse applications, like healthcare and agriculture, since it can capture absorption bands of molecules in different spectral areas. A promising approach for multispectral snapshot imaging are camera arrays. Image processing is necessary to warp all different views to the same view to retrieve a consistent multispectral datacube. This process is also called multispectral image registration. After a cross spectral disparity estimation, an occlusion detection is required to find the pixels that were not recorded by the peripheral cameras. In this paper, a novel fast edge-aware occlusion detection is presented, which is shown to reduce the runtime by at least a factor of 12. Moreover, an evaluation on ground truth data reveals better performance in terms of precision and recall. Finally, the quality of a final multispectral datacube can be improved by more than 1.5 dB in terms of PSNR as well as in terms of SSIM in an existing multispectral registration pipeline. The source code is available at \url{https://github.com/FAU-LMS/fast-occlusion-detection}.
Interactive decision support system for lung cancer segmentation
Authors: Volodymyr Sydorskyi
Date: 2024-08-26 00:00:00
Tasks: Lesion Segmentation, Segmentation

This paper studies Clinical Intelligent Decision Support Systems (CIDSSs) for lung cancer segmentation, which are based on deep neural nets. A new interactive CIDSS is proposed and compared with previous approaches. Addition-ally, the purpose uncertainty problem in building interactive systems is discussed, and criteria for measuring both quality and amount of user feedback are proposed. In order to automate system evaluation, a new algorithm was used to simulate expert feedback. The proposed interactive CIDSS outperforms previous approaches (both interactive and noninteractive) on the task of lung lesion segmentation. This ap-proach looks promising both in terms of quality and expert user experience. At the same time, this paper discusses a bunch of possible modifications that can be done to improve both evaluation criteria and proposed CIDSS in future works.
Learning Multi-Rate Task-Oriented Communications Over Symmetric Discrete Memoryless Channels
Authors: Anbang Zhang, Shuaishuai Guo
Date: 2024-08-24 00:00:00

This letter introduces a multi-rate task-oriented communication (MR-ToC) framework. This framework dynamically adapts to variations in affordable data rate within the communication pipeline. It conceptualizes communication pipelines as symmetric, discrete, memoryless channels. We employ a progressive learning strategy to train the system, comprising a nested codebook for encoding and task inference. This configuration allows for the adjustment of multiple rate levels in response to evolving channel conditions. The results from our experiments show that this system not only supports edge inference across various coding levels but also excels in adapting to variable communication environments.
Joint 9D Receiver Localization and Ephemeris Correction with LEO and $5$G Base Stations
Authors: Don-Roberts Emenonye, Wasif J. Hussain, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2024-08-29 00:00:00
Tasks: Position

This paper leverages Fisher information to examine the interaction between low-Earth orbit (LEO) satellites and 5G base stations (BSs) in enabling 9D receiver localization and refining LEO ephemeris. First, we propose a channel model that incorporates all relevant links: LEO-receiver, LEO-BS, and BS-receiver.Then, we utilize the Fisher information matrix (FIM) to quantify the information available about the channel parameters in these links. By transforming these FIMs, we derive the FIM for 9D receiver localization parameters-comprising 3D position, 3D orientation, and 3D velocity-along with LEO position and velocity offsets. We present closed-form expressions for the FIM entries corresponding to these localization parameters. Our identifiability analysis based on the FIM reveals that: i) With a single LEO, three BSs, and three time slots are required to estimate the 9D localization parameters and correct the LEO position and velocity. ii) With two LEOs, the same configuration (three BSs and three time slots) suffices for both tasks. iii) With three LEOs, three BSs and four time slots are needed to achieve the same goal. A key insight from the Cramer-Rao lower bound (CRLB) analysis is that, under the configuration of one LEO, three BSs, and three time slots, the estimated errors for receiver positioning, velocity, and orientation, as well as LEO position and velocity offsets, are 0.1 cm, 1 mm/s, 0.001 rad, 0.01 m, and 1 m/s, respectively. The receiver localization parameters are estimated after 1 s while the LEO offset parameters are estimated after 20 s. Additionally, our CRLB analysis indicates that operating frequency has minimal impact on receiver orientation estimation accuracy, and the number of receive antennas has a negligible effect on LEO velocity estimation accuracy.
Fundamentals of LEO Based Localization
Authors: Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2024-08-29 00:00:00
Tasks: Position

In this paper, we derive the fundamental limits of low earth orbit (LEO) enabled localization by analyzing the available information in signals from multiple LEOs during different transmission time slots received on a multiple antennas and evaluate the utility of these signals for $9$D localization ($3$D position, $3$D orientation, and $3$D velocity estimation). We start by deriving the Fisher Information Matrix (FIM) for the channel parameters that are present in the signals received from LEOs in the same or multiple constellations during multiple transmission time slots. To accomplish this, we define a system model that captures i) time offset between LEOs caused by having relatively cheap clocks, ii) frequency offset between LEOs, iii) the unknown Doppler rate caused by high mobility LEOs, and iv) multiple transmission time slots from a particular LEO. We transform the FIM for the channel parameters to the FIM for the location parameters and determine the required conditions for localization. To do this, we start with the $3$D localization cases: i) $3$D positioning with known velocity and orientation, ii) $3$D orientation estimation with known position and velocity, and iii) $3$D velocity estimation with known position and orientation. Subsequently, we derive the FIM for the full $9$D localization case ($3$D position, $3$D orientation, and $3$D velocity estimation) in terms of the FIM for the $3$D localization. Using these results, we determine the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas that allow for different levels of location estimation.
Design of stacked intelligent metasurfaces with reconfigurable amplitude and phase for multiuser downlink beamforming
Authors: Donatella Darsena, Francesco Verde, Ivan Iudice, Vincenzo Galdi
Date: 2024-08-29 00:00:00

A novel technology based on stacked intelligent metasurfaces (SIM) has recently emerged. This platform involves cascading multiple metasurfaces, each acting as a digitally programmable physical layer within a diffractive neural network. SIM enable the implementation of signal-processing transformations directly in the electromagnetic wave domain, eliminating the need for expensive, high-precision, and power-intensive digital platforms. However, existing studies employing SIM in wireless communication applications rely solely on nearly passive structures that control only the phase of the meta-atoms in each layer. In this study, we propose a SIM-aided downlink multiuser transmission scheme, where the SIM at the base station (BS) end is designed by combining nearly passive layers with phase-only reconfiguration capabilities and active layers integrated with amplifier chips to enable amplitude control. Our optimal design aims at maximizing the sum rate for the best group of users by jointly optimizing the transmit power allocation at the BS and the wave-based beamforming at the SIM. In addition to the standard sum-power constraint at the BS, our optimization framework includes two additional constraints: (i) a per-stream power preserving constraint to prevent propagation losses across the SIM, and (ii) an amplitude constraint to account for power limitations for each active layer. To further reduce the complexity of the optimal beamforming solution, we explore a simple yet suboptimal zero-forcing (ZF) beamforming design, where the wave-based transformation implemented by the SIM is selected to eliminate interference among user streams. Finally, extensive Monte Carlo simulations demonstrate that incorporating both nearly passive and active layers within the SIM significantly enhances capacity compared to previously reported phase-only coding SIM.
Optimal Weight Scheme for Fusion-Assisted Cooperative Multi-Monostatic Object Localization in 6G Networks
Authors: Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova
Date: 2024-08-29 00:00:00
Tasks: Object Localization

Cooperative multi-monostatic sensing enables accurate positioning of passive targets by combining the sensed environment of multiple base stations (BS). In this work, we propose a novel fusion algorithm that optimally finds the weight to combine the time-of-arrival (ToA) and angle-of-arrival (AoA) likelihood probability density function (PDF) of multiple BSs. In particular, we employ a log-linear pooling function that fuses all BSs' PDFs using a weighted geometric average. We formulated an optimization problem that minimizes the Reverse Kullback Leibler Divergence (RKLD) and proposed an iterative algorithm based on the Monte Carlo importance sampling (MCIS) approach to obtain the optimal fusion weights. Numerical results verify that our proposed fusion scheme with optimal weights outperforms the existing benchmark in terms of positioning accuracy in both unbiased (line-of-sight only) and biased (multipath-rich environment) scenarios.
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver
Authors: Uyoata E. Uyoata, Ramoni O. Adeogun
Date: 2024-08-29 00:00:00
Tasks: Traffic Prediction, Transfer Learning

Deep learning has been used to tackle problems in wireless communication including signal detection, channel estimation, traffic prediction, and demapping. Achieving reasonable results with deep learning typically requires large datasets which may be difficult to obtain for every scenario/configuration in wireless communication. Transfer learning (TL) solves this problem by leveraging knowledge and experience gained from one scenario or configuration to adapt a system to a different scenario using smaller dataset. TL has been studied for various stand-alone parts of the radio receiver where individual receiver components, for example, the channel estimator are replaced by a neural network. There has however been no work on TL for receivers where the entire receiver chain is replaced by a neural network. This paper fills this gap by studying the performance of fine-tuning based transfer learning techniques for various configuration mismatch cases using a deep neural single-input-multiple-output(SIMO) receiver. Simulation results show that overall, partial fine-tuning better closes the performance gap between zero target dataset and sufficient target dataset.