Deepfake Detection Using Multi Modal Approach
Abstract
Deepfake technology is a serious danger to the accuracy of information shared online in the
age of digital communication. This artificial intelligence (AI) produced videos, which may
accurately portray people saying or doing things they never did, have serious ramifications
for digital media credibility, human rights, and public debate. Advanced techniques for
deepfake detection are required due to their potential misuse for espionage, manipulation,
coercion, and harassment. In order to overcome this difficulty, we have created a deepfake
video detector by utilizing CNNs' capabilities. Our method examines video frames for minute
discrepancies that are characteristic of deepfake footage, making use of CNN's powerful
feature extraction capabilities. Our methodology provides a potential remedy for by
concentrating on temporal irregularities and pixel-level differences that are frequently
undetectable to the human sight. This effort not only advances technology in the battle
against digital disinformation, but it also emphasizes how crucial cross-sector cooperation is
to preserving the integrity of online media. Our results shed light on the direction of future
studies and advancements in the industry and demonstrate how important sophisticated
machine learning methods are to preserving the security and legitimacy of digital interactions.
It's important to acknowledge, however, that the fight against deepfakes is an ongoing arms
race. As deepfake creators develop more sophisticated techniques, so too must deepfake
detectors. This necessitates continuous improvement of detection algorithms, collaboration
between researchers and tech companies, and public awareness campaigns to equip users
with critical thinking skills to spot potential deepfakes.