Purpose

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

Design/methodology/approach

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Findings

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

Originality/value

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

中文翻译：

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基于 Taylor-rider 的深度卷积神经网络用于 3D 光照环境中的图像伪造检测

目的

随着照片编辑软件的进步，可以生成假图像，从而降低对数字图像的信任度。可以创建看起来像真实图像的伪造图像，而不会留下任何关于图像更改的视觉线索。图像取证领域引入了多种伪造检测技术，可有效区分假图像和原始图像，以恢复对数字图像的信任。在几幅伪造图像中，涉及人脸的拼接图像更不安全。因此，需要一种伪造检测方法来检测拼接图像。

设计/方法/方法

本文提出了一种基于 Taylor-rider 优化算法的深度卷积神经网络（Taylor-ROA-based DeepCNN），用于检测拼接图像。最初，使用 Viola-Jones 算法检测拼接图像中的人脸，使用基于地标的 3D 可变形模型 (L3DMM) 建立人脸的 3 维 (3D) 形状，该模型估计光系数。然后，根据面部的光系数确定距离度量，例如 Bhattacharya、Seuclidean、Euclidean、Hamming、Chebyshev 和相关系数。这些形成了所提出的基于 Taylor-ROA 的 DeepCNN 的特征向量，它决定了拼接图像。

发现

使用 DSO-1、DSI-1、真实数据集和混合数据集的实验分析表明，该方法获得的最大准确率、真阳性率 (TPR) 和真阴性率 (TNR) 分别为 99%、98.88% 和 96.03% , 对于 DSO-1 数据集。与现有的方法，如 Kee 和 Farid 的阴影形状（SFS )，随机猜测，彭波等人。、神经网络、FOA-SVNN、基于 CNN 的 MBK 和 Manoj Kumar等人。，分别在准确性方面。

原创性/价值

Taylor-ROA 是通过将 Taylor 级数集成到骑手优化算法 (ROA) 中来开发的，以优化调整 DeepCNN。