Video Manipulation Detection Using DenseNet and GoogLeNet

Recent advancements in video technology have democratized facial manipulation techniques, such as FaceSwap and deepfakes, enabling realistic edits with minimal effort. While beneficial in various applications, these tools pose societal risks if misused for spreading disinformation or cyberbullying. The widespread availability of this technology through web and mobile apps has heightened the threat, making the detection of manipulated content a critical challenge. It aims to address the ethical and societal risks associated with the widespread avail- ability of user-friendly facial manipulation tools, such as deepfakes, by focusing on detecting deepfake manipulation in videos. This emphasizes the significance of dataset selection and training methodology for optimal results, employing ensemble learning with DenseNet and GoogLeNet models. The proposed methodology involves training models from scratch and with pre-trained weights, using Celeb- DF (v1) and Face Forensics++ datasets to enhance model generalization, and analyzing video frames to extract features for detecting manipulated faces in videos. The overall goal is to fortify societal defenses against the misuse of facial manipulation tools and promote responsible technological innovation.