DeFakeGuard: An Adaptive API for Comprehensive Deepfake Detection

DefakeGuard. An adaptable API for detecting deepfakes, is a study in a super important field of artificial intelligence and cybersecurity. Why, you ask? Because of the rising prevalence of altered content in digital spaces. Not to mention, the misuse often leads to misinformation and breaches of privacy. This research aspires to create a multi-modal deepfake detection model that examines images, videos, and audio recordings with remarkable accuracy and efficiency. This approach improves the efficiency and adaptability of the system. Our strategy employs XceptionNet, Convolutional Neural Networks (CNNs) and InceptionV3 for strong image and video assessment; meanwhile, we utilize Mel-frequency cepstral coefficients (MFCCs) for audio deepfakes. To boost transparency, Explainable AI (XAI) techniques shed light on why something gets flagged as deepfaked. Moreover, we bring forth an API that incorporates the trained model. This enables smooth integration as an extension for various applications. It offers flexibility and adaptability to all end-users. This system identifies manipulated content, providing incredible accuracy and efficiency.