使用深度学习的人脸检测系统

Prof. Bireshwar Ganguly
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引用次数: 0

摘要

在这项研究中,我们提出了一种利用深度学习技术检测和解释深度伪造图像的新方法。我们的方法将人脸检测、特征提取和注意力可视化结合起来,以提供对人脸图像真实性的见解。我们采用多任务级联卷积神经网络(MTCNN)进行准确的人脸检测,并采用在 VGGFace2 数据集上预训练的 InceptionResnetV1 架构进行特征提取。GradCAM 是一种基于梯度的可视化技术,可突出显示输入图像中对分类决策贡献最大的区域。通过在原始图像上叠加注意力图,我们的方法提供了可解释的说明,使用户能够理解模型的决策过程。实验结果表明,我们的方法在检测深度伪造图像和提供深刻解释方面具有有效性和可解释性,有助于推动深度伪造检测研究的发展。关键词- Python、Python 库、Mtcnn、InceptionResnetV1、Gradio
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Manipulated Face Detection System Using Deep Learning
In this research, we propose a novel approach for detecting and explaining deepfake images using deep learning techniques. Our method utilizes a combination of face detection, feature extraction, and attention visualization to provide insights into the authenticity of facial images. We employ the Multi-Task Cascaded Convolutional Neural Network (MTCNN) for accurate face detection and the InceptionResnetV1 architecture pretrained on the VGGFace2 dataset for feature extraction. The model is further enhanced with GradCAM, a gradient-based visualization technique, to highlight the regions of the input image contributing most to the classification decision. Our approach offers interpretable explanations by overlaying attention maps onto the original images, enabling users to understand the model's decision-making process. Experimental results demonstrate the effectiveness and interpretability of our method in detecting deepfake images and providing insightful explanations, contributing to the advancement of deepfake detection research. Keywords: - python, python libraries, Mtcnn, InceptionResnetV1,Gradio
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