Distinguishing computer-generated images from photographic images using two-stream convolutional neural network
暂无分享,去创建一个
[1] Siwei Lyu,et al. How realistic is photorealistic? , 2005, IEEE Transactions on Signal Processing.
[2] Dong-Ming Yan,et al. Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks , 2018, IEEE Transactions on Information Forensics and Security.
[3] R. Polikar,et al. Bootstrap - Inspired Techniques in Computation Intelligence , 2007, IEEE Signal Processing Magazine.
[4] Wang Rangding,et al. Classifying computer generated graphics and natural image based on image contour information , 2012 .
[5] Larry S. Davis,et al. Learning Rich Features for Image Manipulation Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[8] Kunj Bihari Meena,et al. A Deep Learning Based Method to Discriminate Between Photorealistic Computer Generated Images and Photographic Images , 2020, ICACDS.
[9] Zhenzhen Zhang,et al. Distinguishing computer graphics from photographic images using a multiresolution approach based on local binary patterns , 2014, Secur. Commun. Networks.
[10] Belhassen Bayar,et al. Learning Unified Deep-Features for Multiple Forensic Tasks , 2018, IH&MMSec.
[11] Satoshi Oyama,et al. Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs , 2016, Expert Syst. Appl..
[12] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[13] Jessica J. Fridrich,et al. Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.
[14] Qi Cui. Identifying materials of photographic images and photorealistic computer generated graphics based on deep CNNs , 2018 .
[15] Shiguo Lian,et al. Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics , 2017, Multimedia Tools and Applications.
[16] Yuewei Dai,et al. Photorealistic computer graphics forensics based on leading digit law , 2011 .
[17] Vipin Tyagi,et al. A copy-move image forgery detection technique based on Gaussian-Hermite moments , 2019, Multimedia Tools and Applications.
[18] Vipin Tyagi,et al. Understanding Digital Image Processing , 2018 .
[19] S. P. Ghrera,et al. Pixel-Based Image Forgery Detection: A Review , 2014 .
[20] Vipin Tyagi,et al. Methods to Distinguish Photorealistic Computer Generated Images from Photographic Images: A Review , 2019, ICACDS.
[21] Fei Peng,et al. Identifying photographic images and photorealistic computer graphics using multifractal spectrum features of PRNU , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).
[22] Anderson Rocha,et al. Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach , 2013, J. Vis. Commun. Image Represent..
[23] Hongxia Wang,et al. Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components , 2020, Sensors.
[24] Wei Zhang,et al. Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning , 2018, Sensors.
[25] 何源,et al. A local joint fast handoff scheme in cognitive wireless mesh networks , 2014 .
[26] Dong-Ming Yan,et al. Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images , 2020, Digit. Investig..
[27] Ira Kemelmacher-Shlizerman,et al. Synthesizing Obama , 2017, ACM Trans. Graph..
[28] Jianru Xue,et al. A statistical feature based approach to distinguish PRCG from photographs , 2014, Comput. Vis. Image Underst..
[29] Yun Q. Shi,et al. Distinguishing Computer Graphics from Photographic Images Using Local Binary Patterns , 2012, IWDW.
[30] Ming He,et al. Distinguish computer generated and digital images: A CNN solution , 2018, Concurr. Comput. Pract. Exp..
[31] Dong-Ming Yan,et al. Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation , 2020, Journal of Computer Science and Technology.
[32] Junichi Yamagishi,et al. Distinguishing computer graphics from natural images using convolution neural networks , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).
[33] Xinghao Jiang,et al. Computer Graphics Identification Combining Convolutional and Recurrent Neural Networks , 2018, IEEE Signal Processing Letters.
[34] H. P. Chen,et al. Detecting computer generated images based on local ternary count , 2016, Pattern Recognition and Image Analysis.
[35] Guohua Wu,et al. An Evaluation of Deep Learning-Based Computer Generated Image Detection Approaches , 2019, IEEE Access.
[36] Vipin Tyagi,et al. A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms , 2020, Multimedia Tools and Applications.
[37] Xiangyang Luo,et al. Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain , 2019, IEEE Transactions on Circuits and Systems for Video Technology.
[38] Vipin Tyagi,et al. Image Forgery Detection: Survey and Future Directions , 2019, Data, Engineering and Applications.
[39] Lutz Prechelt,et al. Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.
[40] Loui Al-Shrouf. Development and implementation of a reliable decision fusion and pattern recognition system for object detection and condition monitoring , 2014 .
[41] Frank Piessens,et al. Key Reinstallation Attacks: Forcing Nonce Reuse in WPA2 , 2017, CCS.