Digital Twin and Blockchain for Smart Cities

6Optimized CNN Learning Model With MultiThreading for Forgery Feature Detection in Real-Time Streaming Approaches

Sneha Venkateshalu* and Santosh Deshpande

¹Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, India

Abstract

The concept of internet of things connects everyday consequences to the internet in real time which improves our quality of life and experiences. Processing such real-time data make memory and computations intensive. To acquire efficient outcome, real-time deep-learning convolutional neural network models can overcome challenging tasks related to video analyzing on edge devices. Attaining the robustness of the detection of content manipulation is made possible through multi-threading, which is a sequence entropy with neural network model, to extract the features. The purpose of a convolutional neural network is to analyze and extract the specified required in-depth features. The PRELU activation function is used to make a conversion of the input signal layer into an output signal in the neural network model. This produces the desired outcome with better convergence properties. To resolve small model performance levels caused by under-fitting with high bias, a regularization technique can be applied to the model. Further increasing the work speed of the detection algorithm on edge devices can be done using the multi-threading technique. Modular median filter is one of the widely used digital filter methods worldwide ...