Kolmogorov's theorem and multilayer neural networks
Neural Networks
ACM SIGGRAPH 2004 Papers
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Journal of Machine Learning Research
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Proceedings of the 10th ACM workshop on Multimedia and security
A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation
Proceedings of the 11th ACM workshop on Multimedia and security
Demosaicking by alternating projections: theory and fast one-step implementation
IEEE Transactions on Image Processing
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
How realistic is photorealistic?
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Blind Identification of Source Cell-Phone Model
IEEE Transactions on Information Forensics and Security
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Classification of digital camera-models based on demosaicing artifacts
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Demosaicing: image reconstruction from color CCD samples
IEEE Transactions on Image Processing
Color plane interpolation using alternating projections
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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In this work, we propose a neural network based framework to explore the statistical correlation intrinsically embedded due to interpolations in a relatively small neighborhood, in which the interpolation process is cognized from the interpolation results and the spatially invariant stylized computational rules in interpolation algorithms are simulated and learned by adjusting weights and bias values of neural networks. Experiments show that, our approach is competitive among the state of the art of source camera identification methods. It is also effective for digital forgery detection and other interesting experiments such as the digital demographic diagnosis and prediction. The framework can also be applied to other types of image interpolations such as super-resolution.