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@article{195550,
        author = {Samitha and DR Sreejith vignesh},
        title = {Deep face detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1024-1030},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195550},
        abstract = {The unprecedented development of generative AI makes possible, with unprecedented ease and accuracy, deepfakes that are very difficult to distinguish from human-generated content. This exposes significant risks in domains such as security, politics, journalism, and digital trust. This paper proposes an AI-driven deepfake detection framework that classifies media into manipulated or human-authentic categories with high reliability. This framework combines multimodal analysis that inspects visual artifacts, audio inconsistencies, facial dynamics, and temporal patterns with deep neural network architectures trained on large-scale datasets of real and synthetic media. By embedding feature extraction techniques coupled with anomaly detection models, the proposed approach identifies subtle irregularities created along the generation pipeline of deepfakes. Experimental results demonstrate strong performance across various manipulation types and real-world scenarios, thus underlining robustness and generalization capability. This research contributes to a growing demand for automated, scalable, and trustworthy deepfake detection instruments that will help preserve the integrity of digital content in an age when synthetic media is becoming increasingly practical.},
        keywords = {Deep Fake Detection, Synthetic Media, Manipulated Media, AI Generated Media, Facial Forgery Analysis, Multimedia Forensics, Convolutional Neural Networks, Deep Neural Networks, Feature Extraction, Anomaly Detection, Audio Visual Inconsistencies, Temporal Artifact Analysis, Media Integrity, Digital Authenticity, Fake vs Real Classification, Machine Learning, Computer Vision, Generative Adversarial Networks, Anti Spoofing Techniques},
        month = {April},
        }