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首页> 外文期刊>American journal of applied sciences >DESIGN AN ADVANCE COMPUTER-AIDED TOOL FOR IMAGE AUTHENTICATION AND CLASSIFICATION | Science Publications
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DESIGN AN ADVANCE COMPUTER-AIDED TOOL FOR IMAGE AUTHENTICATION AND CLASSIFICATION | Science Publications

机译：设计提前计算机辅助工具，用于图像身份验证和分类|科学出版物

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摘要

> Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.
机译：在多年来，在解决许多现实生活中，应用了数字图像处理和人工智能领域的进步。这可以在用于安全系统，身份注册的面部图像识别中看到。因此，身份登记的瓶颈是图像处理。这些以使用主成分分析（PCA）和使用离散余弦变换（DCT）的分数（PCA）和图像压缩来以图像预处理的形式进行图像预处理的形式提取。其他处理包括使用对比度拉伸的滤波和直方图均衡，同时将图像作为分析工具的一部分增强。因此，本研究工作提供了一种通用集成图像伪造检测分析工具，使用黑色传播神经网络（BPNN）处理器具有图像面部识别。所提出的设计工具是一种多功能智能工具，具有新颖的可编程误差目标和光强度架构。此外，其前进的双数据库增加了高性能应用的效率。利用这一事实，由于面部图像识别将始终为每个输入图像提供匹配的输出或最接近的输出图像，而不管真实性如何，都提出并设计用于以高精度执行图像伪造检测± 2％的错误率。同时，提出了一种新的结构，为BPNN识别的所有输入测试图像提供有效的自动图像伪造检测。因此，在馈送到识别工具之前将认证输入图像。

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