AI-Powered DeepFake Detection System

The proliferation of deepfake technology—powered by advancements in artificial intelligence and deep learning—has significantly intensified concerns surrounding the authenticity, integrity, and reliability of digital multimedia content. Deepfakes, which involve the manipulation of images and videos to create hyper-realistic but entirely fabricated representations of individuals, have rapidly transitioned from academic curiosities to tools with potential for misuse across political, financial, andpersonal domains. These forgeries often evade traditional detection techniques due to the sophistication of the generative models used to create them, including Generative Adversarial Networks (GANs) and autoencoders. As a result, the urgent need for robust, scalable, and generalizable deepfake detection mechanisms has become paramount in ensuring the security of digital information ecosystems. In response to this growing challenge, this paper presents a novel detection system that integrates twopowerful machine learning architectures—ResNeXt and Long Short-Term Memory (LSTM) networks— to effectively identify manipulated content in both images and videos. ResNeXt, a convolutional neural network (CNN) architecture known for its modular design and superior feature extraction capabilities, is employed to learn spatial and structural patterns indicative of tampering. For video data, we further enhance the system by incorporating LSTM networks, which excel at modeling sequential dependencies and capturing temporal inconsistencies across video frames. This two-tiered architecture allows the system to analyze not only individual image frames but also the subtle temporal artifacts that often emerge due to frame-by-frame manipulation in videos. The proposed system is rigorously evaluated using benchmark datasets, including FaceForensics++ andCeleb-DF for video deepfakes, and a curated image dataset specifically chosen for assessing manipulated still images. These datasets are well-regarded in the field for their quality, diversity, and representativeness of real-world manipulation scenarios.Our research contributes significantly to the fields of media forensics, cybersecurity, and AI ethics by offering a unified and efficient solution for detecting deepfakes across both static and dynamic content. By combining deep spatial feature extraction with temporal sequence modeling, our method addresses the limitations of single-modality detection systems and provides a scalable foundation for future developments in automated media authentication.