Forged File Detection and Steganographic content Identification (FFDASCI) using Deep Learning Techniques

Keywords: object detection, weakly supervised learning, deformable part models, region proposals, deep learning, convolutional neural networks, transfer learning... ages, n’ayant

In this paper, we evaluate the efficiency of using CNNs for source camera model identification based on deep learning and convolutional neural networks.. The contribution repre- sents

Our lentigo detection method combines the InceptionV3 model with other known deep learning techniques like trans- fer learning [13] and data augmentation [14].. 3 presents the

Among deep learning models, convolutional neural networks (CNN) have been proved to work well on image data [2, 6], while stacked autoencoders are more suitable for learning

Firstly, we used different detection architectures based on deep neural networks to generate potential bounding boxes, and then fine-tuned a pre-trained neural network with

Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks.. Nguyen Thanh Hai, Yann Chevaleyre, Edi Prifti, Nataliya Sokolovska,

Overfeat Network [54] was the first deep learning object detector model which used Convolutional Neural Networks (CNNs) along with a sliding window approach..

Our lentigo detection method combines the InceptionV3 model with other known deep learning techniques like trans- fer learning [13] and data augmentation [14].. 3 presents the