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Artificial images and recordings are broad on the web via different media channels such as blogs, YouTube videos, etc. These manipulated and synthesized images tend to steal the identity of individuals and majorly contribute to establishing societal disruptions such as theft, political errors, social engineering, disinformation attacks and reputation fraud. These fake visual objects gradually came to be known as deep fakes. Different deep learning techniques are used to generate deepfake images which go unnoticed by human eyes. It is essential to develop a defense mechanism that can stop the common people from being manipulated and harnessed. The objective of this work is to develop an ensemble deep learning-based system that can differentiate between fake and real images. With the use of the recommended optical flow technique, a novel approach is proposed that extracts the apparent motion of image pixels which gives more accurate results compared to other state-of-the-art. FaceForensics + + dataset is used to test the extraction algorithms and ensemble model which fetched an accuracy of 86.02% for the DeepFake subset and 85.7% for the FaceSwap subset of the dataset. To the best knowledge, no one has completely used the ensemble model- OptiFake on the optical flow derived frames, highlighting a research gap in the field of deepfake detection. [ABSTRACT FROM AUTHOR]

We have proposed MultiLexANFIS which is an adaptive neuro-fuzzy inference system (ANFIS) that incorporates inputs from multiple lexicons to perform sentiment analysis of social media posts. We classify tweets into two classes: neutral and non-neutral; the latter class includes both positive and negative polarity. This type of classification will be considered for applications that aim to test the neutrality of content posted by the users in social media platforms. In our proposed model, features are extracted by integrating natural language processing with fuzzy logic; hence, it is able to deal with the fuzziness of natural language in a very efficient and automatic manner. We have proposed a novel set of 64 rules for the proposed neuro-fuzzy network that can classify tweets correctly by working on fuzzy features fetched from VADER, AFINN and SentiWordNet lexicons. The proposed novel rules are domain independent, i.e., we can extend these rules for any textual data that employs lexicons. The antecedent and consequent parameters of the ANFIS are optimized by gradient descent and least squares estimate algorithms, respectively, in an iterative manner. The key contributions of this paper are: (1) a novel neuro-fuzzy system: MultiLexANFIS that takes as its input the positive and negative sentiment scores of tweets computed from multiple lexicons—VADER, AFINN and SentiWordNet, in order to classify the tweets into neutral and non-neutral content, (2) a novel set of 64 rules for the Sugeno-type fuzzy inference system—MultiLexANFIS, (3) single-lexicon-based ANFIS variants to classify tweets when multiple lexicons are not available and (4) comparison of MultiLexANFIS with different fuzzy, non-fuzzy and deep learning state of the art on various benchmark datasets revealing the superiority of our proposed neuro-fuzzy system for social sentiment analysis. [ABSTRACT FROM AUTHOR]