Banerjee, D.Chittaragi, N.B.Koolagudi, S.G.2026-02-032025Multimedia Tools and Applications, 2025, 84, 31, pp. 38269-3828513807501https://doi.org/10.1007/s11042-025-20715-3https://idr.nitk.ac.in/handle/123456789/20085Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake videos by creating an ensemble network, based on statistical and deep learning methods to detect interframe forgery and intraframe forgery in forged videos separately. In this paper, Noise signature extraction of a particular image capturing sensor and an Autoencoder-based Convolutional Neural Network model (CNN) are used to localize the forged regions. We have trained the model to localize Deepfake video forgeries as well as copy-paste forgeries with effective results in the test data. The proposed fake video detector can be applied at the back-end of on-line video aggregating services and check their authenticity to verify the genuineness of videos. The results achieved have shown better performances in detecting fake videos compared to existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.AuthenticationDeep learningImage codingImage denoisingImage segmentationVideo recordingAdversarial networksDe-noisingDeepfakeFake video detectionGenerative adversarial networkSources identificationsVideo detectionVideo forgeriesVideo source identificationVideo sourcesConvolutional neural networks