Due to the use of powerful computers and advanced software for photo editing, image manipulation in digital images simply degrades the trust in digital images. Image forensic analysis focuses on image authenticity and image content. To process forensic research, different methods are introduced, which effectively differentiate fake images from the original image. A technique named image splicing is commonly used for image tampering, and the tampered image may be used in photography contents, news reports, and so forth, which brings negative influences among the society. Thus, for detecting spliced images, this paper proposed an automatic forgery detection approach named Taylor-adaptive rag-bull rider (RR) optimization algorithm-based deep convolutional neural network (Taylor-RNet). At first, the face of a human is detected from the spliced image using the Viola Jones algorithm, and later, to estimate light coefficients, the three-dimensional (3D) shape of the face is determined by using a landmark-based 3D morphable model (L3DMM). Then, distance measures, like, Bhattacharya, Euclidean, Seuclidean, Chebyshev, correlation coefficients, and Hamming, are determined from the light coefficients that form the feature vector to the proposed Taylor-RNet, which identifies the spliced image. Taylor-adaptive RR is the integration of the Taylor series with the adaptive RR optimization algorithm. Finally, the experimental analysis is performed using four data sets, such as DSO-1, DSI-1, real data set, and hybrid data set. The analysis result of the proposed method obtained a maximum accuracy of 96.921%, true positive rate of 99.981%, and true negative rate of 99.783%.

中文翻译：

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Taylor-RNet：一种使用基于泰勒自适应 rag-bull Rider 的深度卷积神经网络的图像伪造检测方法

由于使用功能强大的计算机和先进的照片编辑软件，数字图像中的图像处理只会降低对数字图像的信任。图像取证分析侧重于图像真实性和图像内容。为了处理取证研究，引入了不同的方法，可以有效地将假图像与原始图像区分开来。图像篡改通常采用图像拼接技术，篡改后的图像可能用于摄影内容、新闻报道等，给社会带来负面影响。因此，针对拼接图像的检测，本文提出了一种基于深度卷积神经网络（Taylor-RNet）的Taylor-adaptive rag-bull Rider（RR）优化算法的自动伪造检测方法。首先，使用 Viola Jones 算法从拼接图像中检测人脸，然后为了估计光系数，使用基于地标的 3D 可变形模型 (L3DMM) 确定人脸的三维 (3D) 形状. 然后，距离度量，如 Bhattacharya、Euclidean、Seuclidean、Chebyshev、相关系数和 Hamming，从形成特征向量的光系数确定到提议的 Taylor-RNet，后者识别拼接图像。泰勒自适应 RR 是泰勒级数与自适应 RR 优化算法的集成。最后，使用DSO-1、DSI-1、真实数据集和混合数据集四个数据集进行实验分析。所提方法的分析结果最高准确率为96.921%，真阳性率为99.981%，