Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A hybrid model of convo-GAN to detect fake images(Grenze Scientific Society, 2021) Saha, S.; Rudra, B.With advancements in the field of Deep Learning, it has become easy to generate face swaps, thereby creating fake images which look extremely realistic, leaving few traces which cannot be detected by bare human eyes. Such images are known as ‘DeepFakes’ that can be used to create a ruckus and affect the quality of public discourse on sensitive issues, defame an individual’s profile, create political distress, blackmail a person or envision fake cyber terrorists. This paper proposes methods to detect fake images with the help of hybrid models having Convolutional Neural Network with Error Level Analysis, Gated Recurrent Unit neural network, Long Short Term Memory recurrent neural network and Generative Adversarial Network respectively. The 2019 ‘Real and Fake Face Detection’ dataset from Kaggle [7] is used to train the models and by experimentation we are able to prove that the combined model of Convolutional Neural Network and Generative Adversarial Network outperforms other models. © Grenze Scientific Society, 2021.Item Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests(Institute of Electrical and Electronics Engineers Inc., 2021) Reddy, S.A.; Rudra, B.Application programming interfaces (APIs) are a vital part of every online business. APIs are responsible for transferring data across systems within a company or to the users through the web or mobile applications. Security is a concern for any public-facing application. The objective of this study is to analyze incoming requests to a target API and flag any malicious activity. This paper proposes a solution using sequence models to identify whether or not an API request has SQL, XML, JSON, and other types of malicious injections. We also propose a novel heuristic procedure that minimizes the number of false positives. False positives are the valid API requests that are misclassified as malicious by the model. © 2021 IEEE.