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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Bhandage, Venkatesh
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Computer Science & IT Engineering

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A deep learning model with an inductive transfer learning for forgery image detection Bevinamarad, Prabhu; H. Unki, Prakash; Bhandage, Venkatesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp801-810

Abstract

Due to the availability of affordable electronic devices and several advanced online and offline multimedia content editing applications, the frequency of image manipulation has increased. In addition, the manipulated images are presented as evidence in courtrooms, circulated on social media and uploaded upon authentication to deceive the situation. This study implements a deep learning (DL) framework with inductive transfer learning (ITL) by using a pre-trained network to benefit from the discovered feature maps rather than starting from scratch and fine-tuning the process to check and classify whether the suspected image is authenticated or forged effectively. To experiment with the proposed model, we used both Columbian uncompressed image splicing detection (CUISD) and the CoMoFoD dataset for training and testing. We measured the model’s performance by changing hyperparameters and confirmed the better selection of values for the hyperparameter to yield compromised results. As per the evaluation results, our model showed improved results by classifying new instances of images with an average precision of 89.00%, recall of 86.43%, F1-score of 87.32, and accuracy of 87.72% and consistently performed better compared to other methods currently in use.
Classification of Bharatanatyam postures using tailored features and artificial neural network Bhandage, Venkatesh; Anami, Basavaraj; J, Andrew; Hadimani, Balachandra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp482-491

Abstract

Bharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures.
Co-Authors Anami, Basavaraj Bevinamarad, Prabhu H. Unki, Prakash Hadimani, Balachandra J, Andrew