Conference Papers
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Item Survey: Neural Network Authentication and Tampering Detection(Springer, 2023) Kumar, R.; P, A.; Naveen, B.; Chandavarkar, B.R.Neural networks have become quite the buzzword in a decade, resulting in extensive research and extensive integration of neural networks in application development. From self-driving vehicles to IoT devices, each such area has seen some form of integration of a neural network(s). Image and video content have found application in medical, forensic, etc. Due to the excessive use of digital content, there has also been a rise in various advanced image editing applications such as Photoshop, making it easier for people to tamper with images. Therefore, coming up with techniques to validate or authenticate images has gained much interest in recent times. Current neural network-based methods can see all kinds of tampering because neural network capability extracts complex features from the images, making them more effective. Thus, in this study, we review some image forgery techniques and look over how neural networks find their application to detect forgery and authenticate images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Light-weight Deep Learning Model for Cataract Detection using Novel Activation Function(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, P.; Naveen, B.; Mohapatra, A.R.; Annappa, B.; Dodia, S.In cataracts, the natural lens behind the iris and pupil is cloudy, which causes light passing through it to be distorted or blocked, causing blurry or dim vision. About 50% of all cases of blindness worldwide is caused by cataract, according to WHO and the National Library of Medicine. A timely diagnosis of cataracts can help prevent vision loss and other disease-related complications. Several recent developments in machine learning have significantly impacted medical science. However, most existing approaches for cataract detection are based on traditional machine learning techniques. There have been a few attempts to use deep learning in recent years; the models have delivered decent outcomes but require much computational power. Reducing ophthalmologists' time can improve patient outcomes, increase access to care, lower costs, address workforce shortages, and improve healthcare efficiency. It allows ophthalmologists to see more patients and provide more accurate, timely diagnoses and treatments. Using lightweight deep learning algorithms, this paper proposes a solution that delivers rapid and precise results without requiring high-end hardware. A novel activation function is also proposed that significantly improved the performance. The proposed model is a lightweight one that achieved 95.8% accuracy using only 16,874 parameters. © 2023 IEEE.