Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

With the recent tremendous advances of computer graphics rendering and image editing technologies,computergenerated fake images,which in general do not reflect what happens in the reality,can now easily deceive the inspection of human visual system.In this work,we propose a convolutional neural network(CNN)-based model to distinguish computergenerated(CG)images from natural images(NIs)with channel and pixel correlation.The key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels explicitly.Unlike previous approaches that directly apply CNN to solve this problem,we consider the generality of the network(or subnetwork),i.e.,the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original networks.Experimental results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification performance.We also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

6D Object Pose Estimation in Cluttered Scenes from RGB Images

We propose a feature-fusion network for pose estimation directly from RGB images without any depth information in this study.First,we introduce a two-stream architecture consisting of segmentation and regression streams.The segmentation stream processes the spatial embedding features and obtains the corresponding image crop.These features are further coupled with the image crop in the fusion network.Second,we use an efficient perspective-n-point(E-PnP)algorithm in the regression stream to extract robust spatial features between 3D and 2D keypoints.Finally,we perform iterative refinement with an end-to-end mechanism to improve the estimation performance.We conduct experiments on two public datasets of YCB-Video and the challenging Occluded-LineMOD.The results show that our method outperforms state-of-the-art approaches in both the speed and the accuracy.