CNN-Based Fast Source Device Identification

Blind source mobile device identification based on recorded call

Mel-frequency cepstrum coefficients (MFCCs) extracted from speech recordings has been proven to be the most effective feature set to capture the frequency spectra produced by a recording device. This paper claims that audio evidence such as a recorded call contains intrinsic artifacts at both transmitting and receiving ends. These artifacts allow recognition of the source mobile device on the o...

Pedestrian Detection Based on Fast R-CNN and Batch Normalization

Most of the pedestrian detection methods are based on hand-crafted features which produce low accuracy on complex scenes. With the development of deep learning method, pedestrian detection has achieved great success. In this paper, we take advantage of a convolutional neural network which is based on Fast R-CNN framework to extract robust pedestrian features for efficient and effective pedestri...

Boundary-based Image Forgery Detection by Fast Shallow CNN

Image forgery detection is the task of detecting and localizing forged parts in tampered images. Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpness of edges. However, a good detection method should also be applicable to low resolution images because compressed or resized images are common these days. To this end, we propo...

Face Identification with Cnn-um

∗ Analogical & Neural Computing Laboratory Computer and Automation Research Institute Lágymányosi u. 11, Budapest, H-1111, Hungary, e-mail: [szlavik,sziranyi]@sztaki.hu, tel.: +36 1 279 7155 Abstract In this paper an analogic face-registration algorithm is proposed for video tracking of surveillance applications using CNN-UM. The algorithm follows pose variations of head by affine transformatio...

Blind Source Computer Device Identification from Recorded Calls