JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Unmasking Deepfake.
Sumanth M V, Arun kumar KL.
This paper presents a method to automatically and efficiently detect face tampering in videos, deepfake has emerged as a significant challenge in the digital age. The system utilizes the convolutional neural network (CNN) to extract frame-level features and detect the fake videos that are created. While deepfakes hold potential for beneficial applications in entertainment, education, and creative arts, their misuse poses serious threats like spread misinformation in the society. These threats encompass the circulate of misinformation, defamation, erosion of trust in digital media, political manipulation, and the potential for unprecedented levels of cybercrimes, Various techniques are employed to distinguish real from fake media, such as analyzing face swapping indicators, detecting behavioral anomalies, and identifying inconsistencies in a person's face expression, this deepfake detector can be adopted by police to find if the video is legit or fake.
Deepfake, Synthetic, convolution neural network (CNN), Long Short-Term Memory (LSTM).