Deepfake Detection Using XceptionNet

This paper presents a precise and highly efficient method for detecting deepfakes, which have become increasingly accessible via mobile applications, posing significant threats to society. Leveraging the Xception network, a state-of-the-art convolutional neural network (CNN), we address the challenge of identifying and categorizing deepfakes in both images and videos. With deepfakes achieving unprece-dented levels of visual fidelity, traditional detection methods are inadequate. The Xception network excels in capturing intricate visual patterns and anomalies indicative of deepfake manipulation. Trained on extensive datasets encompassing both real and deepfake content, it offers exceptional generalization capabilities, enabling accurate classification of previously unseen instances. This research emphasizes the critical need for robust deep fake detection mechanisms to protect against malicious use, ensuring compliance with official rules and ethical standards, and preserving public trust. The proposed Xception- based approach holds promise in addressing this pressing challenge, providing a reliable means to distinguish deepfakes from authentic content, ultimately safeguarding the integrity of digital media and information dissemination.