Search

There is a huge pile of literature, accessible on economic growth (GDP), natural resource rents (NR), mineral rents (MR), natural gas rents (GR), oil rents (OR), and forest rents (FR) but it does not address the association between trade openness (TO) and the availability of these resources. Thus, this study objectively probes the link among these resources and TO in CAREC nations (Georgia, China, Pakistan, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan) from 2000 to 2022. Mongolia, Afghanistan, and Tajikistan from CAREC countries are exempted in this inquiry due to unavailability of the data for the variables, GR, MR, and OR. The key methods used in this inquiry include method of moment quantile regression (MMQR) followed by Westerlund analysis, slope heterogeneity, CIPS unit root test, cross‐sectional dependence (CSD), and matrix correlation. The MMQR yields valid, trustworthy, and robust results. The empirical estimates demonstrate that MR and OR suggest significant positive impacts on TO. However, NR, GR, FR, and GDP have negative association with TO in CAREC countries. Besides, the research offers pertinent theoretical, and managerial blueprint proposals for global commerce by utilizing MR, OR, FR, and NR contributing to sustainable development goals (SDGs) by fostering economic development, promoting sustainable development, and addressing environmental and social challenges, and highlighting the real impact and association of these resources with TO based on empirical aftermath of the study. [ABSTRACT FROM AUTHOR]

Objective:This study aims to evaluate the local efficacy and long-term outcomes of microwave ablation (MWA) for treating large unresectable hepatocellular carcinomas (HCCs). Methods:A total of 82 patients with 5-6 cm unresectable HCCs, who underwent a single MWA procedure during the period of January 2007 to July 2011, were retrospectively enrolled into this study. Percentages of technical success and complications of MWA and HCC local recurrence (LR) after MWA were determined. In addition, prognostic factors were screened and overall survival (OS) and recurrence-free survival (RFS) rates were estimated. Results:One-, three- and five-year OS rates in this MWA-treated cohort were 92.7, 63.4 and 41.1%, respectively; and the corresponding RFS rates were 65.9, 31.7 and 23.0%, respectively. Primary technical efficacy was 89.0% after the first round of ablation, three (3.7%) patients developed major complications, and LR rate was 20.7%. Child-Pugh classification (p < .001), tumour location (p = .049) and LR (p = .002) were independent factors associated with OS, as determined by multivariate analysis. Conclusions:MWA is safe and effective for the treatment of selected large HCCs, and provides an alternative treatment option for patients with unresectable HCCs. Furthermore, the favourable local efficacy of MWA could potentially improve long-term survival. [ABSTRACT FROM AUTHOR]

S-Adenosylhomocysteine hydrolase (SAHH) catalyzes the reversible conversion of S-adenosylhomocysteine into adenosine and homocysteine. The SAHH from Thermotoga maritima ( TmSAHH) was expressed in Escherichia coli and the recombinant protein was purified and crystallized. TmSAHH crystals belonging to space group C2, with unit-cell parameters a = 106.3, b = 112.0, c = 164.9 Å, β = 103.5°, were obtained by the sitting-drop vapour-diffusion method and diffracted to 2.85 Å resolution. Initial phase determination by molecular replacement clearly indicated that the crystal contains one homotetramer per asymmetric unit. Further refinement of the crystal structure is in progress. [ABSTRACT FROM AUTHOR]

Over the past few years, deep generative models have significantly evolved, enabling the synthesis of realistic content and also bringing security concerns of illegal misuse. Therefore, active protection for generative models has been proposed recently, aiming to generate samples with hidden messages for future identification while preserving the original generating performance. However, existing active protection methods are specifically designed for generative adversarial networks (GANs), restricted to handling unconditional image generation. We observe that they get limited identification performance and visual quality when handling audio-driven video generation conditioned on target audio and source input to drive video generation with consistent context, e.g., identity and movement, between frame sequences. To address this issue, we introduce a simple yet effective active Protection framework for Audio-Driven Video Generation, named PADVG. To be specific, we present a novel frame-shared embedding module in which messages to hide are first transformed into frame-shared message coefficients. Then, these coefficients are assembled with the intermediate feature maps of video generators at multiple feature levels to generate the embedded video frames. Besides, PADVG further considers two visual consistent losses: (i) intra-frame loss is utilized to keep the visual consistency with different hidden messages; (ii) inter-frame loss is used to preserve the visual consistency across different video frames. Moreover, we also propose an auxiliary denoising training strategy through perturbing the assembled features by learnable pixel-level noise to improve identification performance, while enhancing robustness against real-world disturbances. Extensive experiments demonstrate that our proposed PADVG for audio-driven video generation can effectively identify the generated videos and achieve high visual quality. [ABSTRACT FROM AUTHOR]

With the continuous advancement of deepfake technology, there has been a surge in the creation of realistic fake videos. Unfortunately, the malicious utilization of deepfake poses a significant threat to societal morality and political security. Therefore, numerous researchers have proposed various deepfake detection methods. However, traditional deepfake approaches tend to focus on specific forgery features, such as artifacts or inconsistent actions, which can be vulnerable to specialized countermeasures. Recent studies show an intrinsic correlation between facial and audio cues, which can be exploited for deepfake detection. To address these challenges and enhance the robustness and generalization of deepfake detection algorithms, we propose a novel joint audio-visual deepfake detection model named AVA-CL, which is capable of detecting deepfakes in both audio and visual domains. Furthermore, exploiting the inherent correlation and consistency between audio and visual enhances the effectiveness of deepfake detection significantly. Through extensive experiments, we demonstrate that our proposed AVA-CL model outperforms many state-of-the-art (SOTA) methods with superior robustness and generalization capabilities. This research presents a promising approach for deepfake detection and reducing the harm caused by malicious use. [ABSTRACT FROM AUTHOR]

Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of these approaches has reached a blockage. In this article, we propose to perform deepfake detection from an unexplored voice-face matching view. Our approach is founded on two supporting points: first, there is a high degree of homogeneity between the voice and face of an individual (i.e., they are highly correlated), and second, deepfake videos often involve mismatched identities between the voice and face due to face-swapping techniques. To this end, we develop a voice-face matching method that measures the matching degree between these two modalities to identify deepfake videos. Nevertheless, training on specific deepfake datasets makes the model overfit certain traits of deepfake algorithms. We instead advocate a method that quickly adapts to untapped forgery, with a pre-training then fine-tuning paradigm. Specifically, we first pre-train the model on a generic audio-visual dataset, followed by the fine-tuning on downstream deepfake data. We conduct extensive experiments over three widely exploited deepfake datasets: DFDC, FakeAVCeleb, and DeepfakeTIMIT. Our method obtains significant performance gains as compared to other state-of-the-art competitors. For instance, ourmethod outperforms the baselines by nearly 2%, achieving an AUC of 86.11% on FakeAVCeleb. It is also worth noting that our method already achieves competitive results when fine-tuned on limited deepfake data. [ABSTRACT FROM AUTHOR]

The advancement of generative models has made it easier to create highly realistic Deepfake videos. This accessibility has led to a surge in research on Deepfake detection to mitigate potential misuse. Typically, Deepfake detection models utilize binary backbones, even though the training dataset contains additional exploitable information, such as the Deepfake generation method employed for each video. However, recent findings suggest that inferring a binary class from a multi-class backbone yields superior performance compared to directly employing a binary backbone. Building upon this research, our article introduces two novel methods to infer a binary class from a multi-class backbone. The first method, named root dummies, leverages the dummy triplet loss, which employs fixed vectors (i.e., dummies) instead of mined positives and negatives in the triplet loss. By training the multi-class backbone with these dummies, we can easily infer a binary class during testing by adjusting the number of dummies (from six during training to two during inference). Through this approach, we achieve an accuracy improvement of 0.23% compared to the existing inference method, without requiring additional training. The second proposed method is transfer learning. It involves training a classifier, such as a support vector machine, to predict binary classes based on the image embeddings generated by the multi-class backbone. Although this method necessitates additional training, it further enhances the model's performance, resulting in an accuracy increase of 1.79%. In summary, our proposed methods improve the accuracy of Deepfake detection by simply modifying the number of classes during training, making them suitable for integration into a variety of existing Deepfake training pipelines. [ABSTRACT FROM AUTHOR]

Deepfake data contains realistically manipulated faces—its abuses pose a huge threat to the security and privacy-critical applications. Intensive research from academia and industry has produced many deepfake/detection models, leading to a constant race of attack and defense. However, due to the lack of a unified evaluation platform, many critical questions on this subject remain largely unexplored. How is the anti-detection ability of the existing deepfake models? How generalizable are existing detection models against different deepfake samples? How effective are the detection APIs provided by the cloud-based vendors? How evasive and transferable are adversarial deepfakes in the lab and real-world environment? How do various factors impact the performance of deepfake and detection models? To bridge the gap, we design and implement DEEPFAKER1 a unified and comprehensive deepfake detection evaluation platform. Specifically, DEEPFAKER has integrated 10 state-of-the-art deepfake methods and 9 representative detection methods, while providing a user-friendly interface and modular design that allows for easy integration of new methods. Leveraging DEEPFAKER, we conduct a large-scale empirical study of facial deepfake/detection models and draw a set of key findings: (i) the detection methods have poor generalization on samples generated by different deepfake methods; (ii) there is no significant correlation between anti-detection ability and visual quality of deepfake samples; (iii) the current detection APIs have poor detection performance and adversarial deepfakes can achieve about 70% attack success rate on all cloud-based vendors, calling for an urgent need to deploy effective and robust detection APIs; (iv) the detection methods in the lab are more robust against transfer attacks than the detection APIs in the real-world environment; and (v) deepfake videos may not always be more difficult to detect after video compression. We envision that DEEPFAKER will benefit future research on facial deepfake and detection. [ABSTRACT FROM AUTHOR]

Advancements in computer vision and deep learning have made it difficult to distinguish deepfake visual media. While existing detection frameworks have achieved significant performance on challenging deepfake datasets, these approaches consider only a single perspective. More importantly, in urban scenes, neither complex scenarios can be covered by a single view nor can the correlation between multiple datasets of information be well utilized. In this article, to mine the new view for deepfake detection and utilize the correlation of multi-view information contained in images, we propose a novel triple complementary streams detector (TCSD). First, a novel depth estimator is designed to extract depth information (DI), which has not been used in previous methods. Then, to supplement depth information for obtaining comprehensive forgery clues, we consider the incoherence between image foreground and background information (FBI) and the inconsistency between local and global information (LGI). In addition, we designed an attention-based multi-scale feature extraction (MsFE) module to extract more complementary features from DI, FBI, and LGI. Finally, two attention-based feature fusion modules are proposed to adaptively fuse information. Extensive experiment results show that the proposed approach achieves state-of-the-art performance on detecting deepfakes. [ABSTRACT FROM AUTHOR]

Recently, advanced development of facial manipulation techniques threatens web information security, thus, face forgery detection attracts a lot of attention. It is clear that both spatial and temporal information of facial videos contains the crucial manipulation traces, which are inevitably created during the generation process. However, most existing face forgery detectors only focus on the spatial artifacts or the temporal incoherence, and they are struggling to learn a significant and general kind of representations for manipulated facial videos. In this work, we propose to construct spatial-temporal graphs for fake videos to capture the spatial inconsistency and the temporal incoherence at the same time. To model the spatial-temporal relationship among the graph nodes, a novel forgery detector named Spatio-Temporal Graph Network (STGN) is proposed, which contains two kinds of graph-convolution-based units, the Spatial Relation Graph Unit (SRGU) and the Temporal Attention Graph Unit (TAGU). To exploit spatial information, the SRGU models the inconsistency between each pair of patches in the same frame, instead of focusing on the low-level local spatial artifacts which are vulnerable to samples created by unseen manipulation methods. And, the TAGU is proposed to model the long-distance temporal relation among the patches at the same spatial position in different frames with a graph attention mechanism based on the inter-node similarity. With the SRGU and the TAGU, our STGN can combine the discriminative power of spatial inconsistency and the generalization capacity of temporal incoherence for face forgery detection. Our STGN achieves state-of-the-art performances on several popular forgery detection datasets. Extensive experiments demonstrate both the superiority of our STGN on intra manipulation evaluation and the effectiveness for new sorts of face forgery videos on cross manipulation evaluation. [ABSTRACT FROM AUTHOR]

Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with "unseen" manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. First, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) view of two different aspects, including the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI view of these two different but intrinsic aspects. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques and achieve very competitive performances. We further conduct experiments on cross-manipulation techniques, and the results of our method show the superior advantages on improving generalization ability. [ABSTRACT FROM AUTHOR]

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee. [ABSTRACT FROM AUTHOR]

The adsorption of crude oil in the tight sandstone reservoir is significant. Occurrence states of adsorbed tight oil and its pore and throat reduction effect are two significant directions in tight oil exploration and development. In this paper, the occurrence state of adsorbed tight oil and its porosity and permeability reduction effect is systematically analyzed based on a detailed description and discussion of occurrence space, states, changes of porosity and permeability, and its controlling factors. Five occurrence states are recognized based on the differences in spatial location and morphology of adsorbed tight oil. Adsorbed oil is distributed in all kinds of reservoir spaces and primarily concentrated in the pore − throat radius < 0.25 μm. The distribution characteristics of reservoir spaces control the occurrence states of adsorbed tight oil. The emulsion form, cluster form, throat form, thin film form, and isolation form are mainly stored in intergranular pores, feldspar dissolved pores, throats, the surface of minerals, and intercrystalline pores, respectively. The massive development of quartz, feldspar, and illite is conducive to the distribution of emulsion form, cluster form, and the throat form of tight oil, while the abundance of clay, especially chlorite, controls the distribution of the thin film form and isolated form of tight oil. The adsorption of tight oil causes clogging in the pore network, resulting in reservoir damage. The adsorption of tight oil in the reservoir leads to increased reservoir density by 0.290 g/cm3 and the reduction of porosity and permeability by 3.14% and 0.321 × 10 − 3 μm2, respectively. And the damage of adsorbed oil to permeability (30.79%) is more severe than that of porosity (19.96%). The effect of pore and throat reduction is more evident in reservoirs with higher content of quartz and feldspar. However, the strong adsorption of clay makes it difficult to separate the tight oil adsorbed on its surface. The nature of adsorbed oil in different occurrence states also can determine the separation efficiency in the extraction process. The adsorbed tight oil with emulsion, cluster, and throat forms is easier to desorb than the isolation and thin film forms. The research results can provide a basis for analyzing the occurrence characteristics and reasonably formulating the development method of tight oil. In addition, it can even provide a new understanding and basis for the densification process of reservoir permeability under different conditions. [ABSTRACT FROM AUTHOR]