Portrait photography splicing detection using ensemble of convolutional neural networks

Abstract

Forged portraits of people are widely used for creating deceitful propaganda of individuals or events in social media, and even for cooking up fake pieces of evidence in court proceedings. Hence, it is very important to find the authenticity of the images, and image forgery detection is a significant research area now. This work proposes an ensemble learning technique by combining predictions of different Convolutional Neural Networks (CNNs) for detecting forged portrait photographs. In the proposed method seven different pretrained CNN architectures such as AlexNet, VGG-16, GoogLeNet, Res-Net-18, ResNet-101, Inception-v3, and Inception-ResNet-v2 are utilized. As an initial step, we fine-tune the seven pretrained networks for portrait forgery detection with illuminant maps of images as input, and then uses a majority voting ensemble scheme to combine predictions from the fine-tuned networks. Ensemble methods had been found out to be good for improving the generalization capability of classification models. Experimental analysis is conducted using two publicly available portrait splicing datasets (DSO-1 and DSI-1). The results show that the proposed method outperforms the state-of-the-art methods using traditional machine learning techniques as well as the methods using single CNN classification models.

Construction of intelligent visual design system based on convolutional neural network under the background of big data

“…It has not only made some breakthroughs and accumulated in the basic theory and technology eld, but also appeared relevant industrial application examples in speci c application elds [2] . Although there are still many debates on the relationship between AI and the essence of art, the involvement of AI in the eld of design is easier to understand [3] . This is mainly determined by the production attribute of design and the tool attribute of arti cial intelligence technology.…”