In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye, leading to poor visual quality. In response to the above challenges, this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD. The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation. Subsequently, the images are fed into the targeted white-box model, and the gradient direction is optimized to compute gradient variance. Additionally, an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation, aiming to maximize adversarial attack performance. Experimental results on two publicly available fingerprint datasets show that our method achieves higher attack transferability and robustness than existing methods, and the perturbation is harder to perceive.

In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res-CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res-CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross-material experiments and cross-sensor experiments. Additionally, we leverage Gradient-weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.

In recent years, deep learning has gained widespread application across diverse fields, including image classification and machine translation. Nevertheless, the emergence of adversarial examples has revealed a vulnerability of deep learning techniques to potential attacks. Despite the introduction of diverse adversarial attack methods, they are still constrained by certain limitations. Specifically, global perturbations are easily discernible by humans, resulting in poor imperceptibility. Additionally, current methods encounter limited transferability due to their reliance on attacking specific models. To address these challenges, this paper proposed a spatial-frequency gradient fusion based model augmentation for adversarial attack. First, we utilize a Gaussian convolution kernel to pinpoint regions in images that exhibit significant pixel variation, aiming to generate locally imperceptible perturbations undetectable by humans. These areas, which we consider as complex texture regions, are ideal for adding perturbations. Then, we design a perceptual similarity constraint to regulate the generation of perturbations in smooth texture regions. Subsequently, to further enhance the transferability of our method, we propose a spatial-frequency gradient fusion based model augmentation, applying random spectral transformation to shift into the frequency domain for narrowing the differences between models. Additionally, we design complex region scaling transformations in the spatial domain, aimed at capturing common features shared across models. Finally, we integrate the gradients from both the spatial and frequency domains, leveraging the strengths of both to empower attack models in effectively simulating the target model. Extensive experiments conducted on ImageNet and CIFAR-10 datasets have shown that our method attains a remarkable black-box attack success rate of up to 93.1%, with a perceptual loss reduction of approximately 8.39%, while also exhibiting stronger robustness.

针对当前指纹识别系统容易遭受伪造指纹欺骗攻击的问题,提出一种基于纹理特征融合的指纹活性检测算法.通过设计边缘纹理增强(ETE)和对称差分统计(SDS)2 种脊线纹理特征描述算子来表示真假指纹的显著性纹理,前者用来提取指纹图像脊线的方向纹理信息,后者用来描述邻域内脊线的频率纹理信息.首先,利用感兴趣区域(ROI)提取算法对指纹图像进行预处理,以消除指纹图像中背景空白噪声的干扰;然后,利用ETE和SDS分别提取指纹的脊线纹理特征;接着,统计上述2 类特征的直方图,描述真假指纹的纹理特征;最后,将生成的特征输入支持向量机(SVM)中进行训练和测试.在 LiveDet 2011 指纹数据集的测试中,分别使用 Biometrika、Italdata、Sagem 3 种传感器,且与Best、韦伯局部描述算子(WLD)、局部相位量化(LPQ)和局部二值模式(LBP)4 种指纹检测算法进行了比较,该文算法的检测性能优于其余方法,能够完成当前的活性检测任务.LiveDet 2013 数据集使用Biometrika、Italdata和Swipe 3 种传感器,通过与WLD、不变梯度直方图(HIG)、统一局部二值模式(ULBP)、深度表征结构优化(DRAO)和Winner 5 种指纹活性检测方法对比,该文算法的指纹活性检测准确率有一定的提升.

Recently, with the widespread application of mobile communication devices, fingerprint identification is the most prevalent in all types of mobile computing. While they bring a huge convenience to our lives, the resulting security and privacy issues have caused widespread concern. Fraudulent attack using forged fingerprint is one of the typical attacks to realize illegal intrusion. Thus, fingerprint liveness detection (FLD) for True or Fake fingerprints is very essential. This paper proposes a novel fingerprint liveness detection method based on broad learning with uniform local binary pattern (ULBP). Compared to convolutional neural networks (CNN), training time is drastically reduced. Firstly, the region of interest of the fingerprint image is extracted to remove redundant information. Secondly, texture features in fingerprint images are extracted via ULBP descriptors as the input to the broad learning system (BLS). ULBP reduces the variety of binary patterns of fingerprint features without losing any key information. Finally, the extracted features are fed into the BLS for training. The BLS is a flat network, which transfers and places the original input as a mapped feature in feature nodes, generalizing the structure in augmentation nodes. Experiments show that in Livdet 2011 and Livdet 2013 datasets, the average training time is about 1 s and the performance of identifying real and fake fingerprints is effect. Compared to other advanced models, our method is faster and more miniature.

This paper uses data from all the listed high-tech enterprises in China, from 2013 to 2018, as the samples employed to study the impact of government subsidies on the innovation of high-tech enterprises, as well as the subsidy mechanism. The mechanism is analysed mainly from the perspectives of resource effect and signal transmission effect. In the theoretical analysis, from the perspective of resource effect, the capital guiding role of government subsidies is considered. In addition, this study creatively discusses the impact of rent-seeking behaviour in combination with China's anti-corruption practice. From the perspective of signal transmission, government subsidies are no longer only interpreted as positive signals of the government being in favour of enterprise financing. This study further believes that government subsidies transmit a signal to the public, encouraging them to strengthen their supervision of subsidised enterprises. A multiple regression model and mediating effect model indicate that government subsidies achieve the purpose of stimulating enterprise innovation. The stimulating effect of government subsidies through financing constraints and signal transmission is 9.48% and 10.16%, respectively. These results are consistent with the positive externality theory and the signal transmission theory. At the end of the paper, several relevant suggestions are presented, according to the current developments.

This paper analyzes the influence of downside risk on defaultable bond returns. By introducing a defaultable bond-trading model, we show that the decline in market risk tolerance and information accuracy leads to trading loss under downside conditions. Our empirical analysis indicates that downside risk can explain a large proportion of the variation in yield spreads and contains almost all valid information on liquidity risk. As the credit level decreases, the explanatory power of downside risk increases significantly. We also investigate the predictive power of downside risk in cross-sectional defaultable bond excess returns using a portfolio-level analysis and Fama-MacBeth regressions. We find that downside risk is a strong and robust predictor for future bond returns. In addition, due to the higher proportion of abnormal transactions in the Chinese bond market, downside risk proxy semi-variance can better explain yield spreads and predict portfolio excess returns than the proxy value at risk.

As a central issue in macro-finance studies, the spanning hypothesis has always been the focus of research. Previous studies have focused on whether this hypothesis holds true in developed markets, while paying little attention to that in emerging markets. Because of their unique monetary systems, governments in most emerging markets play a key role in bond returns. This study identifies macroeconomic factors for forecasting excess returns in emerging government bond markets under spanning hypothesis. We find that in previous research, government intervention factors employed in excess returns forecasting have no additional predictive ability, as they are already incorporated in current yields. Using dynamic factor analysis, we find that macroeconomic information, including pure macroeconomic activities and financial factors, has robust incremental predictive power for in-sample and out-of-sample bond excess returns.

This paper proposes a generalized bond pricing model, accounting for all the effects of credit risk, liquidity risk, and their correlation. We use an informed trading model to specify the bond liquidity payoff and analyze the sources of liquidity risk. We show that liquidity risk arises from reduced information accuracy and market risk tolerance, and it is market risk tolerance that links credit and liquidity. Then, we extend the traditional bond pricing model with only credit risk by incorporating liquidity risk into the framework in which the probabilities of the two risk events are estimated by a joint distribution. Using numerical examples, we analyze the role of the correlation between credit and liquidity in bond pricing, especially during a financial crisis. We document that the varying correlation between default and illiquidity explains the phenomenon of bond death spiral observed in a financial crisis. Finally, we take the US corporate bond market as an example to demonstrate our conclusions.

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

Allocating emission quotas among provinces fairly and efficiently is a critical issue for China. We developed a carbon quota allocation framework at the provincial level considering both the equity and efficiency principles based on a multi-objective non-linear programming model. We established a carbon Gini coefficient and an emission abatement cost function to measure the equity and efficiency of quota allocation, respectively. We then introduced them as objectives into a multi-objective non-linear programming model and obtained the optimal emission quota allocation for 30 provinces in China, by realizing the trade-off between the equity and efficiency principles. Our analysis revealed that Chinese carbon emissions have not yet peaked and that the proportion of carbon emission in each province is similar. Moreover, provinces with higher GDP per capita, carbon intensity, and historical accumulated carbon emissions should shoulder more burden of carbon intensity reduction and higher marginal reduction costs. The rationality analysis indicates that our method outperforms traditional methods according to the principles of both equity and efficiency. We conclude by offering policy recommendations for the establishment of a national unified carbon market.

传统可违约债券定价模型将风险分为流动性风险与违约风险两类，相对违约风险，流动性风险损失的来源更为复杂。通过在传统可违约债券定价理论中引入信息不对称的交易模型，文章将流动性分解为信息风险和危机风险两部分，给出了信息不对称条件下的债券定价模型,并指出两类风险作用机制存在差异--信息风险独立于违约风险，而危机风险与之存在关联性。算例表明，两类风险对债券收益的影响效果确有显著区别，对于信用等级低的公司，信息风险增加会造成绝对高的收益利差，而对于信用等级低的公司，同等程度的风险变化，信息风险增加会形成相对其他风险而言更大的收益利差。因此，无论公司信用水平如何，其信息透明度的增加都有利于融资成本的降低。