Enhancing Digital Image Forgery Detection Using Transfer Learning

Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detecting only one type of forgery, such as image splicing or copy-move that is not applied in real life. This paper proposes an approach, to enhance digital image forgery detection using deep learning techniques via transfer learning to uncover two types of image forgery at the same time, The proposed technique relies on discovering the compressed quality of the forged area, which normally differs from the compressed quality of the rest of the image. A deep learning-based model is proposed to detect forgery in digital images, by calculating the difference between the original image and its compressed version, to produce a featured image as an input to the pre-trained model to train the model after removing its classifier and adding a new fine-tuned classifier. A comparison between eight different pre-trained models adapted for binary classification is done. The experimental results show that applying the technique using the adapted eight different pre-trained models outperforms the state-of-the-art methods after comparing it with the resulting evaluation metrics, charts, and graphs. Moreover, the results show that using the technique with the pre-trained model MobileNetV2 has the highest detection accuracy rate (around 95%) with fewer training parameters, leading to faster training time

Self adjusted security architecture for mobile ad hoc networks (MANETs)

In this work we present a novel security architecture for MANETs that merges the clustering and the threshold key management techniques. The proposed distributed authentication architecture reacts with the frequently changing topology of the network and enhances the process of assigning the node\u27s public key. In the proposed architecture, the overall network is divided into clusters where the clusterheads (CH) are connected by virtual networks and share the private key of the Central Authority (CA) using Lagrange interpolation. Experimental results show that the proposed architecture reaches to almost 95.5% of all nodes within an ad-hoc network that are able to communicate securely, 9 times faster than other architectures, to attain the same results. Moreover, the solution is fully decentralized to operate in a large-scale mobile network.<br /

Integrating Hybrid Rule-Based with Case-Based Reasoning

In this paper, we present an approach integrating neurule-based and case-based reasoning. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit)