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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CommIT (Communication & Information Technology)
Vamsidhar Yendapalli
GITAM University
Computer Science & IT Engineering

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Classification of Deepfake Images Using a Novel Explanatory Hybrid Model Sudarshana Kerenalli; Vamsidhar Yendapalli; Mylarareddy Chinnaiah
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8761

Abstract

In court, criminal investigations and identity management tools, like check-in and payment logins, face videos, and photos, are used as evidence more frequently. Although deeply falsified information may be found using deep learning classifiers, block-box decisionmaking makes forensic investigation in criminal trials more challenging. Therefore, the research suggests a three-step classification technique to classify the deceptive deepfake image content. The research examines the visual assessments of an EfficientNet and Shifted Window Transformer (SWinT) hybrid model based on Convolutional Neural Network (CNN) and Transformer architectures. The classifier generality is improved in the first stage using a different augmentation. Then, the hybrid model is developed in the second step by combining the EfficientNet and Shifted Window Transformer architectures. Next, the GradCAM approach for assessing human understanding demonstrates deepfake visual interpretation. In 14,204 images for the validation set, there are 7,096 fake photos and 7,108 real images. In contrast to focusing only on a few discrete face parts, the research shows that the entire deepfake image should be investigated. On a custom dataset of real, Generative Adversarial Networks (GAN)-generated, and human-altered web photos, the proposed method achieves an accuracy of 98.45%, a recall of 99.12%, and a loss of 0.11125. The proposed method successfully distinguishes between real and manipulated images. Moreover, the presented approach can assist investigators in clarifying the composition of the artificially produced material.
Image-based estimation of surface roughness in Al-7075 drilling Shilpa M. Karegoudra; Vamsidhar Yendapalli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3492-3504

Abstract

Surface roughness is an important quality parameter that affects the performance, durability, and reliability of machined components. Measuring the internal surface roughness of drilled holes using conventional contact-based methods is often difficult. Accessibility of the internal surface is complicated due to the diameter of the drilled hole and the need to interrupt the machining process. To overcome these limitations, the proposed work offers a non-contact method for estimating the surface roughness of drilled Al-7075 using image-based analysis. Drilled surfaces are machined using different speeds and feed rates. High-resolution images of the drilled surface are captured using a custom-built image-capturing setup. Texture and frequency features were extracted using gray-level co-occurrence matrix (GLCM), discrete Fourier transform (DFT), and discrete wavelet transform (DWT) techniques. Surface arithmetic average roughness (Ra) was measured using these extracted features. The estimated roughness parameters were then validated by comparing them against roughness parameters obtained by means of the contact stylus technique. According to the experimental results, the accuracy of the wavelet technique is higher, with mean absolute errors of 0.32 μm compared to those obtained using GLCM and DFT techniques. The findings demonstrate that the proposed image-based framework is a reliable and practical solution for non destructive surface roughness prediction.
Co-Authors Mylarareddy Chinnaiah Shilpa M. Karegoudra Sudarshana Kerenalli