Coexistence of Ferromagnetism and Superconductivity at KTaO3 Heterointerfaces.

The coexistence of superconductivity and ferromagnetism is a long-standing issue in superconductivity due to the antagonistic nature of these two ordered states. Experimentally identifying and characterizing novel heterointerface superconductors that coexist with magnetism presents significant challenges. Here, we report the observation of two-dimensional long-range ferromagnetic order in a KTaO3 heterointerface superconductor, showing the coexistence of superconductivity and ferromagnetism. Remarkably, our direct current superconducting quantum interference device measurements reveal an in-plane magnetization hysteresis loop persisting above room temperature. Moreover, first-principles calculations and X-ray magnetic circular dichroism measurements provide decisive insights into the origin of the observed robust ferromagnetism, attributing it to oxygen vacancies that localize electrons in nearby Ta 5d states. Our findings suggest KTaO3 heterointerfaces as time-reversal symmetry breaking superconductors, injecting fresh momentum into the exploration of the intricate interplay between superconductivity and magnetism enhanced by the strong spin-orbit coupling inherent to the heavy Ta in 5d orbitals.

Biomechanical effect of metal augment and bone graft on cup stability for acetabular reconstruction of total hip arthroplasty in hip dysplasia: a finite element analysis.

BACKGROUND: Different methods of acetabular reconstruction with total hip arthroplasty (THA) for Crowe II and III of adult developmental dysplasia of the hip (DDH) acetabular bone defect have been implemented clinically. However, the biomechanical effect of different augmented materials for acetabular reconstruction in THA on shell stability has never been discussed. METHODS: In the present study, autologous bone graft (BG)and metal (Ti6Al4V) augment (MA) were simulated with several acetabular bone defect models of DDH in THA. The contact pressure and micromotion between the shell and host bone were measured for evaluating the shell stability using a finite element method. RESULTS: The peak contact stress between shell and host bone was higher in the MA situation (12.45 vs 8.71 MPa). And the load transfer path was different, for BG models, the high local contact stresses were found at the junction of bone graft and host bone while for MA models the concentrated contact stresses were at the surface of MA. The peak relative micromotion between shell and host bone was higher in the MA situation (12.61 vs 11.13 µm). However, the peak micromotion decreased in the contact interface of MA and cup compared to the BG models. CONCLUSIONS: The higher micromotion was found in MA models, however, enough for bone ingrowth, and direct stronger fixation was achieved in the MA-cup interface. Thus, we recommended the MA can be used as an option, even for Crowe III, however, the decision should be made from clinical follow-up results.

Design of Insulation Tape Tension Control System of Transformer Winding Machine Based on Fuzzy PID.

With the rapid development of science and technology as well as the comprehensive societal progress, the demand for electricity in all walks of life is also increasing. As is known to all, the mechanical structure and tension control of a transformer winding machine is the key to improving the quality of coil winding, due to coil winding being generally considered the core technology of transformer manufacturing. Aiming at the synchronous winding control problem of the conductor and insulating layer of the transformer winding machine, this paper presents a mechanical structure and tension control scheme of a new type of transformer winding machine. Based on the dynamic analysis and modeling of the mechanical structure of the winding machine, the speed control of the main speed roller by the fuzzy PID control rate is implemented initially. Combined with the actual demand of the project, the feasibility and effectiveness of the control target with different tension are verified by the simulation experiment and further compared with the traditional PID control method. The simulation results show that the proposed fuzzy PID control rate can realize the automatic and efficient winding of the transformer winding machine, showing that it is superior to the traditional PID control rate in overcoming the disturbance and controlling effect.

Development and optimization of doxorubicin loaded poly(lactic-co-glycolic acid) nanobubbles for drug delivery into HeLa cells.

Microbubbles (MBs, usually 2-8 microm) as ultrasound contrast agent and drug carrier are promising for ultrasonic imaging and drug delivery. However, MBs posed some limitations due to their large diameters. In the current study, we developed a nanoscale bubbles (nanobubbles, NBs) by encapsulating the doxorubicin (DOX) into poly(lactic-co-glycolic acid) (PLGA) shells (denoted as DOX-PLGA NBs) for drug delivery into cancer cells. The size, morphology, particle stability, drug encapsulation efficiency, and drug payload were determined. The results showed that the DOX-PLGA NBs were uniform (270 +/- 3 nm) and spherical with a smooth surface, and were well dispersed and stable in water. The encapsulation efficiency and payload of DOX increased with its initial loading concentrations. The release behavior of DOX from the DOX-PLGA NBs exhibited a biphasic pattern characterized by an initial burst release followed by a slower and continuous release at both pH 7.4 and pH 4.4, and also presented in a pH-triggered releasing profile. The qualitative analysis of cellular internalization into HeLa cells by inverted fluorescence microscope showed that the cellular uptake of DOX-PLGA NBs was both concentration- and time-dependent. Moreover, the cell viability was also investigated using CCK-8 assay. It was found that DOX-PLGA NBs showed greater HeLa cell growth inhibition effect in vitro compared with free DOX. It was concluded that the DOX-PLGA NBs were biocompatible and appropriate for anti-cancer drug delivery, and were potentially promising as a new therapeutic system for cancer treatment.

Robust distributed fixed-time cooperative hunting control for multi-quadrotor with obstacles avoidance.

This paper addressed the robust distributed fixed-time cooperative hunting problem of multiple quadrotors subject to disturbances in obstacles environment. To handle the underactuated issue inherent in quadrotor dynamics, an inner-outer (attitude-position) loop cascade control configuration is proposed to achieve the cooperative flight control of quadrotors. For position subsystem, as the information of target cannot be accessible to all quadrotors, a distributed fixed-time observer is devised to estimate the target's information. To improve the system's robustness, a fixed-time extended state observer is designed to reject disturbances actively. Based on two observers, the sliding mode position encirclement control protocol with repulsive force is presented to avoid obstacles and encircle the target within a fixed time. For attitude subsystem, a sliding mode attitude tracking control protocol is proposed such that tracking errors can converge to zero even under disturbances. The stability analysis is performed to show the stabilization of the whole closed-loop system with fixed-time convergence. Finally, two sets of comparison simulation are provided to show the superiority of the developed control strategy.

Deep learning-based 3D brain multimodal medical image registration.

Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information extraction capabilities during training and effectively prevents information loss. Additionally, we introduce a lightweight and residual network module at the network's base, which enhances learning ability without significantly increasing training parameters. To evaluate the superiority of our registration model, we utilize evaluation metrics such as structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE). Experimental results demonstrate that our proposed network structure outperforms current state-of-the-art methods, yielding better performance in multimodal registration tasks. Furthermore, generalization testing on databases outside of the training set has confirmed the optimal registration effectiveness of our model.

L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.

In recent years, deep learning has ushered in significant development in medical image registration, and the method of non-rigid registration using deep neural networks to generate a deformation field has higher accuracy. However, unlike monomodal medical image registration, multimodal medical image registration is a more complex and challenging task. This paper proposes a new linear-to-nonlinear framework (L2NLF) for multimodal medical image registration. The first linear stage is essentially image conversion, which can reduce the difference between two images without changing the authenticity of medical images, thus transforming multimodal registration into monomodal registration. The second nonlinear stage is essentially unsupervised deformable registration based on the deep neural network. In this paper, a brand-new registration network, CrossMorph, is designed, a deep neural network similar to the U-net structure. As the backbone of the encoder, the volume CrossFormer block can better extract local and global information. Booster promotes the reduction of more deep features and shallow features. The qualitative and quantitative experimental results on T1 and T2 data of 240 patients' brains show that L2NLF can achieve excellent registration effect in the image conversion part with very low computation, and it will not change the authenticity of the converted image at all. Compared with the current state-of-the-art registration method, CrossMorph can effectively reduce average surface distance, improve dice score, and improve the deformation field's smoothness. The proposed methods have potential value in clinical application.

DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios.

PURPOSE: 3D multimodal medical image deformable registration plays a significant role in medical image analysis and diagnosis. However, due to the substantial differences between images of different modalities, registration is challenging and requires high computational costs. Deep learning-based registration methods face these challenges. The primary aim of this paper is to design a 3D multimodal registration network that ensures high-quality registration results while reducing the number of parameters. METHODS: This study designed a Dual-Encoder More Lightweight Registration Network (DELR-Net). DELR-Net is a low-complexity network that integrates Mamba and ConvNet. The State Space Sequence Module and the Dynamic Large Kernel block are used as the main components of the dual encoders, while the Dynamic Feature Fusion block is used as the main component of the decoder. RESULTS: This study conducted experiments on 3D brain MR images and abdominal MR and CT images. Compared to existing registration methods, DELR-Net achieved better registration results while maintaining a lower number of parameters. Additionally, generalization experiments on other modalities showed that DELR-Net has superior generalization capabilities. CONCLUSION: DELR-Net significantly improves the limitations of 3D multimodal medical image deformable registration, achieving better registration performance with fewer parameters.

Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.

The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients' head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function. Four different single-sequence MR images and one multi-sequence MR image were used to evaluate the accuracy of sCT. During the model testing phase, five testing scenarios were employed to further assess the mean absolute error, peak signal-to-noise ratio, structural similarity index, and root mean square error between the actual CT images and the sCT images generated by different models. T1 sequence-based sCT achieved better results in single-sequence MR-based sCT. Multi-sequence MR-based sCT achieved better results with T1 sequence-based sCT in terms of evaluation metrics. For metrological evaluation, the global gamma passage rate of sCT based on sequence MR was greater than 95% at 3%/3 mm, except for sCT based on T2 sequence MR. We developed a CycleGAN method to synthesize CT using different MR sequences, this method shows encouraging potential for dosimetric evaluation.

Efficacy and Safety of Transarterial Chemoembolization Plus Donafenib with or without Immune Checkpoint Inhibitors as the First-Line Treatment for Unresectable Hepatocellular Carcinoma: A Propensity Score Matching Analysis.

Purpose: To compare the efficacy and safety of transarterial chemoembolization (TACE) plus donafenib with immune checkpoint inhibitors (ICIs) (T+D+I) versus TACE plus donafenib (T+D) as the first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Methods: This retrospective study included patients with unresectable HCC who received T+D+I or T+D between June 2021 and February 2023. The tumor response was analyzed according to the modified Response Evaluation Criteria in Solid Tumors. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse events (TRAEs) in the two groups were compared before and after propensity score matching (PSM). Cox's proportional-hazards regression model was used to analyze factors affecting PFS and OS. Results: This study included 69 patients: 41 patients in the T+D group and 28 patients in the T+D+I group. After PSM, 26 patients in each group were analyzed. Patients in the T+D+I group had a higher DCR (96.2% vs 73.1%, P = 0.021), longer median PFS (13.1 vs 7.2 months, P = 0.017), and longer median OS (23.1 vs 14.7 months, P = 0.021) than those in the T+D group. The ORR in the two groups was similar (53.8% vs 50.0%, P = 0.781). Multivariate analyses revealed that T+D+I treatment and total bilirubin levels of <20 µmol/L were independent prognostic factors for long PFS. T+D+I treatment, Child-Pugh class A, and single-lobe tumor distribution were independent prognostic factors for long OS. The incidence of TRAEs in the two groups was similar (P > 0.05). Conclusion: In comparison with TACE plus donafenib, TACE plus donafenib with ICIs could significantly improve DCR, PFS, and OS as a potential first-line treatment for unresectable HCC with an acceptable safety profile.

Study of visual SLAM methods in minimally invasive surgery.

In recent years, minimally invasive surgery has developed rapidly in the clinical practice of surgery and has gradually become one of the critical surgical techniques. Compared with traditional surgery, the advantages of minimally invasive surgery include small incisions and less pain during the operation, and the patients recover faster after surgery. With the expansion of minimally invasive surgery in several medical fields, traditional minimally invasive techniques have bottlenecks in clinical practice, such as the inability of the endoscope to determine the depth information of the lesion area from the two-dimensional images obtained, the difficulty in locating the endoscopic position information and the inability to get a complete view of the overall situation in the cavity. This paper uses a visual simultaneous localization and mapping (SLAM) approach to achieve endoscope localization and reconstruction of the surgical region in a minimally invasive surgical environment. Firstly, the K-Means algorithm combined with the Super point algorithm is used to extract the feature information of the image in the lumen environment. Compared with Super points, the logarithm of successful matching points increased by 32.69%, the proportion of effective points increased by 25.28%, the error matching rate decreased by 0.64%, and the extraction time decreased by 1.98%. Then the iterative closest point method is used to estimate the position and attitude information of the endoscope. Finally, the disparity map is obtained by the stereo matching method, and the point cloud image of the surgical area is finally recovered.

Exploring native and non-native English speaker teachers' perceptions of English teacher qualities and their students' responses.

Due to globalization, English has gradually become a lingua franca, leading to a rising demand for proficient English teachers all over the globe. In China, more EFL teachers are being recruited, particularly at the tertiary level, with a greater preference for so-called "native English speaking teachers (NESTs)" over "non-native English-speaking teachers (NNESTs)" due to the impacts of native-speakerism. Research has shown NESTs, NNESTs, and students are often misaligned in terms of beliefs about language learning and teaching which affect teaching effectiveness as well as student achievement. Recognizing this issue, this study investigated NESTs', NNESTs', and Chinese English-major students' perceptions of characteristics of effective EFL teachers at four mid-tier universities across China. Findings from semi-structured interviews with 16 students suggest that NNESTs and Chinese English-major students had similar views on language learning and teaching. Both groups valued prerequisite qualities such as having expert knowledge, language skills, teaching skills, and professionalism. NESTs, however, valued qualities such as caring, patience, flexibility, engagement, and awareness of students' learning needs. These differences are likely the result of these two groups of teachers' linguistic, cultural, and educational background differences. The highly uniform views of the two groups of teachers suggest that they tended to emphasize certain qualities while disregarding others. These findings suggest the need to raise teachers' and students' awareness of the benefits of different types of teacher qualities so that curriculum design and lesson planning can be implemented for better instructional alignment to ultimately improve teaching effectiveness.

Semi-supervised lane detection for continuous traffic scenes.

OBJECTIVE: This article aims to upgrade the lane detection algorithm from image to video level in order to advance automatic driving technology. The objective is to propose a cost-efficient algorithm that can handle complex traffic scenes and different driving speeds using continuous image inputs. METHODS: To achieve this objective, we introduce the Multi-ERFNet-ConvLSTM network framework, which combines Efficient Residual Factorized ConvNet (ERFNet) and Convolution Long Short Term Memory (ConvLSTM). Additionally, we incorporate the Pyramidally Attended Feature Extraction (PAFE) Module into our network design to effectively handle multi-scale lane objects. The algorithm is evaluated using a divided dataset and comprehensive assessments are conducted across multiple dimensions. RESULTS: In the testing phase, the Multi-ERFNet-ConvLSTM algorithm surpasses the primary baselines and demonstrates superior performance in terms of Accuracy, Precision, and F1-score metrics. It exhibits excellent detection results in various complex traffic scenes and performs well at different driving speeds. CONCLUSIONS: The proposed Multi-ERFNet-ConvLSTM algorithm provides a robust solution for video-level lane detection in advanced automatic driving. By utilizing continuous image inputs and incorporating the PAFE Module, the algorithm achieves high performance while reducing labeling costs. Its exceptional accuracy, precision, and F1-score metrics highlight its effectiveness in complex traffic scenarios. Moreover, its adaptability to different driving speeds makes it suitable for real-world applications in autonomous driving systems.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm.

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.

Improving performance of medical image alignment through super-resolution.

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.

Synthetic CT generation from CBCT using double-chain-CycleGAN.

PURPOSE: Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical application in adaptive radiotherapy. To explore the potential application value of CBCT in adaptive radiotherapy, In this study, we improve the cycle-GAN's backbone network structure to generate higher quality synthetic CT (sCT) from CBCT. METHOD: An auxiliary chain containing a Diversity Branch Block (DBB) module is added to CycleGAN's generator to obtain low-resolution supplementary semantic information. Moreover, an adaptive learning rate adjustment strategy (Alras) function is used to improve stability in training. Furthermore, Total Variation Loss (TV loss) is added to generator loss to improve image smoothness and reduce noise. RESULTS: Compared to CBCT images, the Root Mean Square Error (RMSE) dropped by 27.97 from 158.49. The Mean Absolute Error (MAE) of the sCT generated by our model improved from 43.2 to 32.05. The Peak Signal-to-Noise Ratio (PSNR) increased by 1.61 from 26.19. The Structural Similarity Index Measure (SSIM) improved from 0.948 to 0.963, and the Gradient Magnitude Similarity Deviation (GMSD) improved from 12.98 to 9.33. The generalization experiments show that our model performance is still superior to CycleGAN and respath-CycleGAN.

A deformable patch-based transformer for 3D medical image registration.

PURPOSE: Medical image registration is of great importance in clinical medicine. However, medical image registration algorithms are still in the development stage due to the challenges posed by the related complex physiological structures. The objective of this study was to design a 3D medical image registration algorithm that satisfies the need for high accuracy and speed of complex physiological structures. METHODS: We present a new unsupervised learning algorithm, "DIT-IVNet," for 3D medical image registration. Unlike the more popular convolution-based U-shaped registration network architectures like VoxelMorph, DIT-IVNet uses a combined convolution and transformer network architecture. To better extract image information features and reduce the heavy training parameters, we improved the 2D_Depatch module to a 3D_Depatch module, thus replacing the patch embedding in the original Vision Transformer which adaptively performs patch embedding based on 3D image structure information. We also designed inception blocks in the down-sampling part of the network to help coordinate feature learning from images to different scales. RESULTS: Dice score, Negative Jacobian determinant, Hausdorff distance, and Structural Similarity evaluation metrics were used to evaluate the registration effects. The results showed that our proposed network had the best metric results compared with some state-of-the-art methods. Moreover, our network obtained the highest Dice score in the generalization experiments which indicated better generalizability of our model. CONCLUSION: We proposed an unsupervised registration network and evaluated its performance in deformable medical image registration. The results of the evaluation metrics showed that the network structure outperformed state-of-the-art methods for the registration of brain datasets.

Quantification of epicardial fat volume using low-dose cardiac scan in patients with low calcium score.

Background: We investigated the accuracy of quantifying epicardial adipose tissue volume (EATV) using low-dose cardiac scan (EATVcardiac scan) and evaluated its clinical utility in predicting coronary heart disease in patients with low or mild calcification. Methods: In total, 204 patients with clinical symptoms of coronary heart disease and coronary artery calcium score (CACS) of <100 AU were enrolled in this retrospective study. After obtaining EATVcardiac scan and EATV measured using computed tomography angiography (EATVCTA), the agreement between the two measurements was evaluated using Pearson correlation coefficient and Bland-Altman analysis. Multivariate logistic regression was used to analyze the utility of EATV in predicting plaque and vulnerable plaque. Receiver operating characteristic curves were constructed. Results: The mean EATVcardiac scan (101.51±41.57 cm3) and EATVCTA (104.57±41.34 cm3) of all patients were similar, and the two measurements were strongly correlated (r=0.9596, P<0.001). The difference between EATVcardiac scan and EATVCTA was -3.0549, with only 4.9% (10/204) of patients having values outside the 95% confidence interval (CI) range (-26.15 to 20.04; P for agreement =0.0003). Further, a significant agreement was observed between EATVcardiac scan and EATVCTA in 126 patients with plaques, with an estimated difference of -3.354, and 6.35% (8/126) of patients had values outside the 95% CI range (-31.37 to 24.66; P for agreement =0.0095). After adjustment for age and sex, EATVcardiac scan and EATVCTA were significantly associated with plaque (all P values <0.001), and the areas under the curve (AUCs) were 0.662 and 0.670 (P=0.4331), respectively. In contrast, EATVcardiac scan and EATVCTA were not associated with vulnerable plaque (P>0.05), with AUCs of 0.550 and 0.530, respectively (P=0.2157). Conclusions: The study results indicate that EATVcardiac scan and EATVCTA are equivalent. In addition, both methods provide comparable values for predicting coronary arteriosclerosis in patients with low-to-mild calcification (CACS of <100 AU).

Post-disaster building damage assessment based on improved U-Net.

When a severe natural disaster occurs, the extraction of post-disaster building damage information is one of the methods to quickly obtain disaster information. The increasingly mature high-resolution remote sensing technology provides a solid foundation for obtaining information about building damage. To address the issues with inaccurate building positioning in existing building damage assessment methods, as well as poor classification due to similar minor and major damage characteristics in building damage classification. Based on U-Net, we designed a two-stage building damage assessment network. The first stage is an independent U-Net focused on building segmentation, followed by a Siamese U-Net focused on building damage classification. The Extra Skip Connection and Asymmetric Convolution Block were used for enhancing the network's ability to segment buildings on different scales; Shuffle Attention directed the network's attention to the correlation of buildings before and after the disaster. The xBD dataset was used for training and testing in the study, and the overall performance was evaluated using a balanced F-score (F1). The improved network had an F1 of 0.8741 for localization and F1 of 0.7536 for classification. When compared to other methods, it achieved better overall performance for building damage assessment and was able to generalize to multiple disasters.

Deepfake Video Detection Based on EfficientNet-V2 Network.

As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and fake videos that seriously endanger the stability of the country and society. Examples include faking politicians to make inappropriate statements, using face-swapping technology to spread false information, and creating fake videos to obtain money. In view of this social problem, based on the original fake face detection system, this paper proposes using a new network of EfficientNet-V2 to distinguish the authenticity of pictures and videos. Moreover, our method was used to deal with two current mainstream large-scale fake face datasets, and EfficientNet-V2 highlighted the superior performance of the new network by comparing the existing detection network with the actual training and testing results. Finally, based on improving the accuracy of the detection system in distinguishing real and fake faces, the actual pictures and videos are detected, and an excellent visualization effect is achieved.