• Mohammed Khaja Rehanuddin
• Mohammad Aftab Saaquib
• Mettu Likitha Reddy
• Mohammed Hassan Ali
• Dr. K. Rajeshwar Rao

Sophisticated and devious deep-fake technology, approaching, is a clear threat to the integrity of digital content. Rigorous detection mechanisms — an immediate need. GuardianAI: A Comprehensive Approach to Deep Fake Detection in Videos using Deep Learning Algorithms presents itself, aiming to address this very concern with the utilization of profound learning and machine learning algorithms. An exploration of the problem ensues, from literature reviews about deep fakes and their detection to the development of profound learning models capable of detecting video fakes from the real deal. On cue, intricate video-frame features are extracted, after thorough data preparation and model implementation. The result: a custom profound-learning ecosystem, designed for deep-fake spotting. 80. 44%, our model accuracy on the test dataset, depicting a hopeful picture. Preventing the rise of deep-fake content, this finding's effectiveness can be followed. Moving forward, the work of our project now creates stepping stones for future endeavors. Refining those models, developing more feature extraction techniques, and deep-fake methodology evolution adaptation, are a few to be named. Innovation should not cease, nor should the spirit of collaboration. Why? The aspiration remains steadfast and true - enhancing digital media's trustworthiness while interrupting, or at least hindering, the torrent of deep-fake content.

"GuardianAI: A Comprehensive Approach to Deep Fake Detection in videos using Deep Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g182-g256, April-2024, Available :http://www.jetir.org/papers/JETIRTHE2105.pdf

"GuardianAI: A Comprehensive Approach to Deep Fake Detection in videos using Deep Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg182-g256, April-2024, Available at : http://www.jetir.org/papers/JETIRTHE2105.pdf