Unmasking Deepfake Images and Videos: A Comprehensive Detection

Deepfake images and videos, which are hyper-realistic but fake, are a growing concern. These images and videos originated as a form of entertainment, but over time, they’ve been misused for nefarious purposes. As technology has advanced, distinguishing deepfakes from real images and videos has become increasingly challenging. This paper presents a comprehensive strategy to address the deepfake problem. Recent advances in AI, machine learning, and deep learning have given rise to innovative tools for manipulating multimedia content. While these technologies have legitimate uses, they’ve also enabled the creation of highly convincing deepfake images and videos. These realistic yet deceptive images and videos, known as “Deepfakes,” pose a significant threat, especially with the recent surge in accessible machine learning-based software tools. These tools make it simple to generate convincing face swaps in photos and movies while leaving little trace of manipulation. Deepfakes can spread misinformation, incite discord, and facilitate harassment and blackmail, compromising the integrity of digital media. In order to tackle this issue, we have developed a pipeline for temporally aware detection that can recognize deepfake photos and videos automatically. In order to extract frame-level features, which form the foundation for recognizing potentially deepfake content, our method uses a Multi-task Cascaded Convolutional Neural Network (MTCNN-CNN). Modern EfficientNet architecture serves as the foundation for our suggested model, which has been modified for better performance. Our ultimate goal is to mitigate the threats posed by deepfakes, protect the integrity of digital media, and raise public awareness about the dangers of manipulated content.