Browsing by Author "Shetty, N."

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Addressing Diffusion Model Based Counter-Forensic Image Manipulation for Synthetic Image Detection
(Association for Computing Machinery, 2024) Herur, A.N.; Santhosh, V.; Shetty, N.; Seelamantula, C.S.
With the rapid development of modern generative models, the need for an automated synthetic image detection process has never been greater. Recent works in the field of synthetic image detection focus on improving out-of-distribution (OoD) classification performance and robustness to common image pre-processing techniques. However, in this work, we intend to explore the nature of an intricate counter-forensic attack, i.e., the reconstruction of real images with Diffusion Model autoencoders, which could be used to adversely affect the performance of modern synthetic image detection algorithms. We present a variety of experiments to study the nature of this counter-forensic attack and use the inferences from these experiments to develop multiple algorithms to detect such reconstructed images while attempting to detect real and purely synthetic images accurately. To do so, we make use of trained classifiers that can detect real images, autoencoder-reconstructed images, and purely synthetic images. Furthermore, we combine these techniques to build a novel ensemble algorithm that competes with state-of-the-art (SoTA) algorithms in the â€˜Real vs. Fakeâ€™ image detection task, while detecting autoencoder reconstructed images accurately, attaining an accuracy of 99.2% in the multiclass setting. Â© 2024 Copyright held by the owner/author(s).
Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite
(2016) Shetty, N.; Herbert, M.A.; Shetty, R.; Shetty, D.S.; Vijay, G.S.
Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use. 2016 Elsevier B.V. All rights reserved.
Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite
(Elsevier Ltd, 2016) Shetty, N.; Herbert, M.A.; Shetty, R.; Shetty, D.S.; Vijay, G.S.
Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use. © 2016 Elsevier B.V. All rights reserved.