• Velpooru Venkata Sai Thushar
• varun shiva krishna Rupani
• Kondadi Tejith

The term deep-fake emerged towards the end of 2017 within the reddit community a popular an American social media platform focused on content rating and discussions since then deep-fake technology has rapidly advanced enabling the crafting incredibly lifelike media material that convincingly mimics individuals appearances however its potential for misuse including the spread of disinformation and deception of users has sparked major apprehensions in response researchers have been actively exploring novel approaches to develop effective deep-fake detection systems this summary provides an overview of the latest advancements in employing machine learning techniques for deep-fake detection aiming to mitigate the risks associated with its misuse. The proposed approach trains machine learning models on a broad dataset that contains both real and deep-fake material it uses essential characteristics taken from eye movements and facial expressions to distinguish between real and fake information the focus of the study is on using deep neural networks and making sure that they can adapt to new developments in deep-fake generating technology. To check the model's precision, accuracy, and recall validation and testing are done on separate datasets. Ethical questions linked to privacy and consent are important to the study framework, addressing concerns connected with the analysis of media material. The research stresses the continual improvement of detection algorithms, including updates to defeat developing deep fake tactics efficiently. The outputs of this research seek to contribute to the development of practical and ethical deep fake detection technologies. As the threat landscape advances, it is vital to be at the forefront of technical innovations to prevent the malicious use of synthetic media. This research gives useful information for the continuing attempts to limit the hazards related with deep fake propagation.

Deepfake Detection, Multi-task Cascaded Convolutional Networks (MTCNN), InceptionresnetV1, GradCAM, Gardio

"DEEP-FAKE DETECTION : MACHINE LEARNING APPROACHES TO COMBAT DEEP-FAKE THREATS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a231-a237, March-2024, Available :http://www.jetir.org/papers/JETIR2403030.pdf

"DEEP-FAKE DETECTION : MACHINE LEARNING APPROACHES TO COMBAT DEEP-FAKE THREATS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa231-a237, March-2024, Available at : http://www.jetir.org/papers/JETIR2403030.pdf