In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deep fake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deep fake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. This may threaten the public, therefore detection of deep fake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and also consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and Wild Deep fake. The accuracy for FFHQ datasets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82% respectively.

Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time,which can delay decision making.Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data,often yielding insights into the data more quickly and in a way more understandble to lay decision makers .SPC and its primary tool-the control chart-provide researchers and practitioners with a method of better understanding and communicating data from software reliability improvement process efforts .This paper provides an s-shaped software reliability growth model based on the Non-Homogenous Poisson Process (NHPP).The maximum likelihood approach is used to estimate the unknown parameters of the model.

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To assess the software reliability by statistical means yields efficient results. In this paper, for an effective monitoring of failure process we have opted Sequential Probability Ratio Test (SPRT) over the time between every r th failure (r is a natural number >=2) instead of inter-failure times. This paper projects a controlling framework based on order statistics of the cumulative quantity between observations of time domain failure data using mean value function of Inflection S-Shaped Model. The two unknown parameters can be estimated using the Maximum Likelihood Estimation (MLE).

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