… Forensic, it is an art and science, in which different type of analysis is involved. The basic
purpose of Forensic … the scope of Forensic Science, the video on the crime scene, images of the …

… Contrasting with recent divergent surveys, this paper enfolds notable developments in
techniques of passive image forensic analysis employing deep learning techniques. Furthermore, …

… After feature extraction and feature transformation, we have used random forest… random
forest-based multiclass ensemble classifier with decision tree as base learner. Random forest …

… The extracted numeric image descriptors trains three learner algorithms for … of Random
forest algorithm and SqueezeNet image embedder as the best for steganalysis in digital forensics …

… The input image size to those CNN pretrained model is 224 × 224. Secondly, we will use
the DRF with four forests two random forests and two completely-random tree forests, having …

… As can be seen, Random Forest starts to produce results better than 70% in terms of F1-score
from the point in which 70% weight is given to the image forensic features, and 30% to the …

… After the feature extraction, we introduce a learning phase using a Random Forest Classifier.
Experimental results, specifically on the Columbia database, show very good results in …

… images, improving the capacity to predict whether an image … a Random Forest classifier.
The results indicate it is possible to differentiate between pornographic images and bikini images…

… The idea behind passive image forensic tools is that non-invertible image … random forest
classifier without further optimization using feature vectors extracted from a set of training images…

… In this paper, Random Forest machine learning algorithm is implemented on the extracted
features of digital images using GoogleNet deep learning model for image forgery detection. …