Biblio

With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenarios and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.

A Local Area Network (LAN) consists of wireless mobile nodes that can communicate with each other through electromagnetic radio waves. Mobile Ad hoc Network (MANET) consists of mobile nodes, the network is infrastructure less. It dynamically self organizes in arbitrary and temporary network topologies. Security is extremely vital for MANET. Attacks pave way for security. Among all the potential attacks on MANET, detection of wormhole attack is very difficult.One malicious node receives packets from a particular location, tunnels them to a different contagious nodes situated in another location of the network and distorts the full routing method. All routes are converged to the wormhole established by the attackers. The complete routing system in MANET gets redirected. Many existing ways have been surveyed to notice wormhole attack in MANET. Our proposed methodology is a unique wormhole detection and prevention algorithm that shall effectively notice the wormhole attack in theMANET. Our notion is to extend the detection as well as the quantitative relation relative to the existing ways.