摘要翻译：
多年来，数字图像处理和人工智能领域的进步已经应用于解决许多现实生活中的问题。这可以在安全系统的面部图像识别、身份登记中看到。因此，身份配准的一个瓶颈是图像处理。通过图像预处理、裁剪提取图像区域、主成分分析(PCA)提取特征和离散余弦变换(DCT)进行图像压缩。其他处理包括滤波和直方图均衡，使用对比度拉伸来执行，同时增强图像作为分析工具的一部分。因此，本研究工作提出了一个通用的综合图像伪造检测分析工具，并利用反向传播神经网络(BPNN)处理器进行图像面部识别。所设计的工具是一种多功能智能工具，具有可编程的误差目标和光强结构。此外，其先进的双数据库提高了高性能应用程序的效率。针对人脸图像识别无论真伪与否，都会对每一幅输入图像给出一个匹配或最接近的输出图像这一事实，提出并设计了通用智能GUI工具，以2%的错误率进行图像伪造检测。同时，提出了一种新的结构，为BPNN识别提供了一种高效的自动伪造图像的检测方法。因此，输入图像在被馈送到识别工具之前将被认证。
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英文标题：
《Design an Advance computer-aided tool for Image Authentication and
  Classification》
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作者：
Rozita Teymourzadeh, Amirize Alpha Laadi, Yazan Samir, Masuri Othman
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  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 includes 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 Back 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 of a 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.
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PDF链接：
https://arxiv.org/pdf/1808.02085