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  1. 2841

    Deepfake detection method based on patch-wise lighting inconsistency by Wenxuan WU, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

    Published 2023-02-01
    “…The rapid development and widespread dissemination of deepfake techniques has caused increased concern.The malicious application of deepfake techniques also poses a potential threat to the society.Therefore, how to detect deepfake content has become a popular research topic.Most of the previous deepfake detection algorithms focused on capturing subtle forgery traces at pixel level and have achieved some results.However, most of the deepfake algorithms ignore the lighting information before and after generation, resulting in some lighting inconsistency between the original face and the forged face, which provided the possibility of using lighting inconsistency to detect deepfake.A corresponding algorithm was designed from two perspectives: introducing lighting inconsistency information and designing a network structure module for a specific task.For the introduction of lighting task, a new network structure was derived by designing the corresponding channel fusion method to provide more lighting inconsistency information to the network feature extraction layer.In order to ensure the portability of the network structure, the process of feature channel fusion was placed before the network extraction information, so that the proposed method can be fully planted to common deepfake detection networks.For the design of the network structure, a deepfake detection method was proposed for lighting inconsistency based on patch-similarity from two perspectives: network structure and loss function design.For the network structure, based on the characteristic of inconsistency between the forged image tampering region and the background region, the extracted features were chunked in the network feature layer and the feature layer similarity matrix was obtained by comparing the patch-wise cosine similarity to make the network focus more on the lighting inconsistency.On this basis, based on the feature layer similarity matching scheme, an independent ground truth and loss function was designed for this task in a targeted manner by comparing the input image with the untampered image of this image for patch-wise authenticity.It is demonstrated experimentally that the accuracy of the proposed method is significantly improved for deepfake detection compared with the baseline method.…”
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  2. 2842

    YOLOv7-CWFD for real time detection of bolt defects on transmission lines by Lincong Peng, Kerui Wang, Hao Zhou, Yi Ma, Pengfei Yu

    Published 2025-01-01
    “…The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. …”
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  3. 2843

    Esthetical Properties of Single-Shade and Multishade Composites in Posterior Teeth by Graziela R. Batista, Alessandra B. Borges, Rayssa F. Zanatta, Cesar R. Pucci, Carlos R. G. Torres

    Published 2023-01-01
    “…Then, class I preparations were made, and each tooth was restored twice, using two different composites of MS/opacity layering material (Admira Fusion—Voco) and an SS/opacity bulk-fill composite (Admira Fusion X-tra—Voco). …”
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    High-performance CT features supporting accurate pre-operative tumor staging in colon cancer by Jianhua Yuan, Cangzheng Jin, Jianrong Si, Baobao Liu, Xiaohan Si, Jianzhi Chen

    Published 2025-02-01
    “…We aimed to pathologically interpret the key CT findings in order to identify reliable markers for pre-treatment staging of colon cancer.Patients and methodsThe following CT features from 136 colon adenocarcinomas were analyzed: colon wall pliability, outline contour, pericolic fat attenuations and vascularity, tumor fusion with adjacent organs, ascites, tumor size, and relevance between tumor and retroperitoneal fascia. …”
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