Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100 accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.
Gando et al @cite_18 presented a deep learning method based on a fine-tuned deep convolutional neural network. This method can automatically distinguish illustrations from photographs and achieve 96.8 Rahmouni et al @cite_20 presented a custom pooling layer to extract statistical features and a CNN framework to distinguish computer-generated graphics from real photographic images. A weighted voting scheme was used to aggregate the local estimates of class probabilities and predict the label of the whole picture. The best accuracy in @cite_20 is 93.2
@cite_18 @cite_20
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This paper presents a deep-learning method for distinguishing computer generated graphics from real photographic images. The proposed method uses a Convolutional Neural Network (CNN) with a custom pooling layer to optimize current best-performing algorithms feature extraction scheme. Local estimates of class probabilities are computed and aggregated to predict the label of the whole picture. We evaluate our work on recent photo-realistic computer graphics and show that it outperforms state of the art methods for both local and full image classification. Automatically detecting illustrations is needed for the target system.Deep Convolutional Neural Networks have been successful in computer vision tasks.DCNN with fine-tuning outperformed the other models including handcrafted features. Systems for aggregating illustrations require a function for automatically distinguishing illustrations from photographs as they crawl the network to collect images. A previous attempt to implement this functionality by designing basic features that were deemed useful for classification achieved an accuracy of only about 58 . On the other hand, deep neural networks had been successful in computer vision tasks, and convolutional neural networks (CNNs) had performed good at extracting such useful image features automatically. We evaluated alternative methods to implement this classification functionality with focus on deep neural networks. As the result of experiments, the method that fine-tuned deep convolutional neural network (DCNN) acquired 96.8 accuracy, outperforming the other models including the custom CNN models that were trained from scratch. We conclude that DCNN with fine-tuning is the best method for implementing a function for automatically distinguishing illustrations from photographs. Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. Then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. The experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods. Multi-label image classification is a challenging problem in computer vision. Motivated by the recent development in image classification performance using Deep Neural Networks, in this work, we propose a flexible deep Convolutional Neural Network (CNN) framework, called Local-Global-CNN (LGC), to improve multi-label image classification performance. LGC consists of firstly a local level multi-label classifier which takes object segment hypotheses as inputs to a local CNN. The output results of these local hypotheses are aggregated together with max-pooling and then re-weighted to consider the label co-occurrence or interdependencies information by using a graphical model in the label space. LGC also utilizes a global CNN that is trained by multi-label images to directly predict the multiple labels from the input. The predictions of local and global level classifiers are finally fused together to obtain MAP estimation of the final multi-label prediction. The above LGC framework could benefit from a pre-train process with a large-scale single-label image dataset, e.g., ImageNet. Experimental results have shown that the proposed framework could achieve promising performance on Pascal VOC2007 and VOC2012 multi-label image dataset.