Biblio

Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.

This paper is the first to investigate the long-term stability of Surface Acoustic Wave Gyroscopes (SAWG) using an ovenized control system. Monolithic integration of a MEMS heater adjacent to SAW devices on Lithium Niobate over insulator substrate (LNOI) tightly couples frequency-based temperature detection with heating for temperature and frequency stabilization. This first prototype demonstrates the ability to minimize the temperature variations of the SAWG to below ±10 μK and stabilize the SAWG resonance frequency to ±0.2 ppm. This approach thus eliminates the thermal drift in a SAWG and enables the development of a new generation of MEMS-based gyroscopes with long-term stability.