Huaxiao Mo
ABSTRACT
Generative Adversarial Network (GAN) is a prominent generative model that are widely used in various applications. Recent studies have indicated that it is possible to obtain fake face images with a high visual quality based on this novel model. If those fake faces are abused in image tampering, it would cause some potential moral, ethical and legal problems. In this paper, therefore, we first propose a Convolutional Neural Network (CNN) based method to identify fake face images generated by the current best method [20], and provide experimental evidences to show that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. In addition, we provide comparative results evaluated on some variants of the proposed CNN architecture, including the high pass filter, the number of the layer groups and the activation function, to further verify the rationality of our method.
- Mart'ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et almbox. . 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016).Google Scholar
- Belhassen Bayar and Matthew C Stamm . 2016. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. 5--10. Google ScholarDigital Library
- David Berthelot, Tom Schumm, and Luke Metz . 2017. Began: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017).Google Scholar
- Gang Cao, Yao Zhao, Rongrong Ni, and Xuelong Li . 2014. Contrast enhancement-based forensics in digital images. IEEE transactions on information forensics and security Vol. 9, 3 (2014), 515--525. Google ScholarDigital Library
- Jiansheng Chen, Xiangui Kang, Ye Liu, and Z Jane Wang . 2015. Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters Vol. 22, 11 (2015), 1849--1853.Google ScholarCross Ref
- Mo Chen, Vahid Sedighi, Mehdi Boroumand, and Jessica Fridrich . 2017. JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images ACM Workshop on Information Hiding and Multimedia Security. 75--84. Google ScholarDigital Library
- Hak-Yeol Choi, Han-Ul Jang, Dongkyu Kim, Jeongho Son, Seung-Min Mun, Sunghee Choi, and Heung-Kyu Lee . {n. d.}. Detecting composite image manipulation based on deep neural networks IEEE International Conference on Systems, Signals and Image Processing. 1--5.Google Scholar
- Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur . 2017. A learned representation for artistic style. In Proceedings of International Conference on Learning Representations.Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio . 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680. Google ScholarDigital Library
- Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa . 2017. Globally and locally consistent image completion. ACM Transactions on Graphics Vol. 36, 4 (2017), 107:1--107:14. Google ScholarDigital Library
- Justin Johnson, Alexandre Alahi, and Li Fei-Fei . 2016. Perceptual losses for real-time style transfer and super-resolution European Conference on Computer Vision. Springer, 694--711.Google Scholar
- Diederik P. Kingma and Jimmy Ba . 2014. Adam: A Method for Stochastic Optimization. CoRR Vol. abs/1412.6980 (2014). showeprint{arxiv}1412.6980deftempurl%http://arxiv.org/abs/1412.6980 tempurlGoogle Scholar
- Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et almbox. . 2017. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4681--4690.Google Scholar
- Haodong Li, Weiqi Luo, Xiaoqing Qiu, and Jiwu Huang . 2018. Identification of various image operations using residual-based features. IEEE Transactions on Circuits and Systems for Video Technology Vol. 28, 1 (2018), 31--45. Google ScholarDigital Library
- Lu Li, Jianru Xue, Zhiqiang Tian, and Nanning Zheng . 2013. Moment feature based forensic detection of resampled digital images Proceedings of the 21st ACM international conference on Multimedia. ACM, 569--572. Google ScholarDigital Library
- Xiaoqing Qiu, Haodong Li, Weiqi Luo, and Jiwu Huang . 2014. A universal image forensic strategy based on steganalytic model Proceedings of the 2nd ACM workshop on Information hiding and multimedia security. ACM, 165--170. Google ScholarDigital Library
- Yuan Rao and Jiangqun Ni . 2016. A deep learning approach to detection of splicing and copy-move forgeries in images IEEE International Workshop on Information Forensics and Security. 1--6.Google Scholar
- Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár . 2017. Amortised map inference for image super-resolution Proceedings of International Conference on Learning Representations.Google Scholar
- Matthew Stamm and KJ Ray Liu . 2008. Blind forensics of contrast enhancement in digital images IEEE International Conference on Image Processing. IEEE, 3112--3115.Google Scholar
- Samuli Laine Jaakko Lehtinen Tero Karras, Timo Aila . 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation. International Conference on Learning Representations (2018). deftempurl%https://openreview.net/forum?id=Hk99zCeAb tempurl accepted as oral presentation.Google Scholar
- Guanshuo Xu, Han Zhou Wu, and Yun Qing Shi . 2016. Structural Design of Convolutional Neural Networks for Steganalysis. IEEE Signal Processing Letters Vol. 23, 5 (2016), 708--712.Google ScholarCross Ref
- Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, and Hao Li . 2017. High-resolution image inpainting using multi-scale neural patch synthesis The IEEE Conference on Computer Vision and Pattern Recognition, Vol. Vol. 1. 3.Google Scholar
Index Terms
- Fake Faces Identification via Convolutional Neural Network
Recommendations
Human Action Recognition using Pre-trained Convolutional Neural Networks
VSIP '20: Proceedings of the 2020 2nd International Conference on Video, Signal and Image ProcessingRecognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. ...
Cucumber leaf disease identification with global pooling dilated convolutional neural network
Highlights- Dilated convolution kernel enlarges local receptive field and enhances feature extraction.
- Global pooling layer reduces training parameters number and avoids overfitting problem.
- Multi-scale convolutional kernels extract multi-...
AbstractIt is a challenging research topic to identify plant disease based on diseased leaf image processing techniques due to the complexity of the diseased leaf images. Deep learning models are promising for identifying plant disease based on leaf ...
Towards dropout training for convolutional neural networks
Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper ...
Comments