Digital Image Forensics
Computational Imaging
Computer Vision
Fourier Transforms
Machine Learning
Contender for the IEEE Signal Processing Cup 2018. An algorithm that identifies which camera was used to capture an image using traces of information left intrinsically in the image, using filters, followed by a deep neural network on these filters. Detection of the source camera from a given image using noise, Fast Fourier Transformations, and other parameters, to train Deep Learning Models in an ensemble.
Technologies Used: Computational Imaging, Signal Processing, Deep Learning, Digital Signal Processing.
My Role:
- Implemented Gray Level Dependency Matrix to get image statistics per camera.
- Dataset augmentation by introducing image manipulations.
- Implemented the Gallagher and Chen Algorithm and Discrete Fourier Transforms to detect periodicity of camera noise.
- Experimented with ensemble learning to achieve the best accuracy.