Computer Vision

List of Articles Computer Vision

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1 - DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation
Adeep Biswas Debayan Bhattacharya Kakelli Anil Kumar
10.52547/jist.9.35.161

20.1001.1.23221437.2021.9.35.2.1

The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media More

The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy. Manuscript profile
Open Access Article
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2 - An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
Ramesh G Prasanna G B Santosh V Bhat Chandrashekar Naik Champa H N
10.52547/jist.9.35.169

20.1001.1.23221437.2021.9.35.3.2

Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and m More

Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used. Manuscript profile

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List of Articles Computer Vision