Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks

Automatic Speaker Verification (ASV) systems are increasingly used in voice bio-metrics for user authentication but are susceptible to logical and physical spoofing attacks, posing security risks. Existing research mainly tackles logical or physical attacks separately, leading to a gap in unified spoofing detection. Moreover, when existing systems attempt to handle both types of attacks, they often exhibit significant disparities in the Equal Error Rate (EER). To bridge this gap, we present a Parallel Stacked Aggregation Network that processes raw audio. Our approach employs a split-transform-aggregation technique, dividing utterances into convolved representations, applying transformations, and aggregating the results to identify logical (LA) and physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC datasets shows the effectiveness of the proposed system. It outperforms state-of-the-art solutions, displaying reduced EER disparities and superior performance in detecting spoofing attacks. This highlights the proposed method's generalizability and superiority. In a world increasingly reliant on voice-based security, our unified spoofing detection system provides a robust defense against a spectrum of voice spoofing attacks, safeguarding ASVs and user data effectively

Securing Voice Biometrics: One-Shot Learning Approach for Audio Deepfake Detection

The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent activities using audio deepfakes, also known as logical-access voice spoofing attacks. These deepfakes pose a concerning threat to voice biometrics due to recent advancements in generative AI and speech synthesis technologies. While several deep learning models for speech synthesis detection have been developed, most of them show poor generalizability, especially when the attacks have different statistical distributions from the ones seen. Therefore, this paper presents Quick-SpoofNet, an approach for detecting both seen and unseen synthetic attacks in the ASV system using one-shot learning and metric learning techniques. By using the effective spectral feature set, the proposed method extracts compact and representative temporal embeddings from the voice samples and utilizes metric learning and triplet loss to assess the similarity index and distinguish different embeddings. The system effectively clusters similar speech embeddings, classifying bona fide speeches as the target class and identifying other clusters as spoofing attacks. The proposed system is evaluated using the ASVspoof 2019 logical access (LA) dataset and tested against unseen deepfake attacks from the ASVspoof 2021 dataset. Additionally, its generalization ability towards unseen bona fide speech is assessed using speech data from the VSDC dataset

AXM-Net: Cross-Modal Context Sharing Attention Network for Person Re-ID

Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align inter-modality representations according to semantic information present for a person and ignore background information. In this work, we present AXM-Net, a novel CNN based architecture designed for learning semantically aligned visual and textual representations. The underlying building block consists of multiple streams of feature maps coming from visual and textual modalities and a novel learnable context sharing semantic alignment network. We also propose complementary intra modal attention learning mechanisms to focus on more fine-grained local details in the features along with a cross-modal affinity loss for robust feature matching. Our design is unique in its ability to implicitly learn feature alignments from data. The entire AXM-Net can be trained in an end-to-end manner. We report results on both person search and cross-modal Re-ID tasks. Extensive experimentation validates the proposed framework and demonstrates its superiority by outperforming the current state-of-the-art methods by a significant margin

Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward

Malicious actors may seek to use different voice-spoofing attacks to fool ASV systems and even use them for spreading misinformation. Various countermeasures have been proposed to detect these spoofing attacks. Due to the extensive work done on spoofing detection in automated speaker verification (ASV) systems in the last 6-7 years, there is a need to classify the research and perform qualitative and quantitative comparisons on state-of-the-art countermeasures. Additionally, no existing survey paper has reviewed integrated solutions to voice spoofing evaluation and speaker verification, adversarial/antiforensics attacks on spoofing countermeasures, and ASV itself, or unified solutions to detect multiple attacks using a single model. Further, no work has been done to provide an apples-to-apples comparison of published countermeasures in order to assess their generalizability by evaluating them across corpora. In this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions to detect speech synthesis (SS), voice conversion (VC), and replay attacks. Additionally, we also review integrated solutions to voice spoofing evaluation and speaker verification, adversarial and anti-forensics attacks on voice countermeasures, and ASV. The limitations and challenges of the existing spoofing countermeasures are also presented. We report the performance of these countermeasures on several datasets and evaluate them across corpora. For the experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of the test bed can be found in our GitHub Repository

Are ESG Stocks Safe-Haven during COVID-19?

This study contributes to the debate on safe-haven characteristics of environmental, social, and governance (ESG) stocks during COVID-19 pandemic. Using wavelet coherence framework on four major ESG stock indices from global and emerging stock markets, and two proxies of COVID-19 fear over the period from February 5th, 2020, to March 18th, 2021, we find a strong and positive co-movement between health fear index of COVID-19 and returns on ESG stocks suggesting the existence of safe-haven properties in ESG stocks. However, we also observe a negative co-movement between stock market base proxy of COVID-19 and returns on ESG indices, suggesting that safe-haven properties of ESG stocks are contingent upon the proxy of COVID-19 pandemic. Our findings are of particular interest for the investors and asset managers who may use ESG stocks to diversify their portfolios during health crisis due to COVID-19 pandemic

Do Stock Market Fear And Economic Policy Uncertainty Co-Move With Covid-19 Fear? Evidence From The Us And Uk

Purpose - The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread. Design/methodology/approach - This study uses wavelet coherence approach because it allows to observe lead-lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021. Findings - The results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indicts than the COVID-19 fear index (C19FI). This study also finds that the UK\u27s implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movemmit of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study\u27s robustness analysis using partial wavelet coherence provides further strengths to the findings. Research limitations/implications - The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK\u27s stock markets. Originality/value - This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et at, 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead-lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al, 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020

Safe-haven properties of soft commodities during times of Covid-19

We use wavelet coherence analysis on global COVID-19 fear index and, soft commodities’ spot and futures prices to investigate safe-haven properties of soft commodities over the period from January 28, 2020 to April 29, 2021. Our findings show that each of the sampled soft commodities shows safe-haven behavior in one of the spot or futures markets and for one of the short-term or long-term investors during the times of COVID-19. Our results also show that safe-haven properties of soft commodities are contingent upon the nature of the commodity. The findings of our mean-variance portfolio analysis indicate that the portfolios with commodity futures are less risky and efficient compared to the portfolio containing stocks only, thus robustly supporting the safe-haven properties of soft commodities during COVID-19. Our results not only have important implications for individual investors and asset managers in suggesting particular soft commodities to strengthen safe-haven and diversification features of their portfolios but also can assist the policy makers to understand and disentangle health fear dimension of several interlocking dynamics affecting the spot and futures prices of soft commodities during COVID-19

The Impact of Taxpayer Perception on Tax Compliance moderated by adoption of e-tax system

The underlying study focuses on investigating that if the impact of taxpayer perceptions have on tax compliance with moderating impact of adopting of e-tax system in Pakistan where Fairness perception, tax knowledge and tax complexity are taken as Independent variables, implementation of e-tax system as a moderating variable and tax compliance as dependent variable. Following a deductive approach, this research study has collected data through self-administered questionnaire from a sample of 163 subjects selected on the basis of convenience sampling technique. The data were analyzed through SPSS and multiple regression technique has been applied on the data. The outcomes suggest that fairness perceptions and tax compliance have direct relationship. The findings of the study suggest that the moderation relationship between the variable named adoption of e-tax system and tax perceptions and tax compliance is insignificant. Besides this, tax knowledge and tax complexity does not have a significant positive impact on tax compliance in Pakistan

The Impact of Taxpayer Perception on Tax Compliance moderated by adoption of e-tax system

The underlying study focuses on investigating that if the impact of taxpayer perceptions have on tax compliance with moderating impact of adopting of e-tax system in Pakistan where Fairness perception, tax knowledge and tax complexity are taken as Independent variables, implementation of e-tax system as a moderating variable and tax compliance as dependent variable. Following a deductive approach, this research study has collected data through self-administered questionnaire from a sample of 163 subjects selected on the basis of convenience sampling technique. The data were analyzed through SPSS and multiple regression technique has been applied on the data. The outcomes suggest that fairness perceptions and tax compliance have direct relationship. The findings of the study suggest that the moderation relationship between the variable named adoption of e-tax system and tax perceptions and tax compliance is insignificant. Besides this, tax knowledge and tax complexity does not have a significant positive impact on tax compliance in Pakistan