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@article{154598,
        author = {Dr. Kiran Y C and Darshan V Prasad and Harsha M and N Navneeth Krishna and Sanjay T C},
        title = {A Survey on Deepfake Detection Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {703-706},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154598},
        abstract = {Deep learning algorithms have recently expanded their applications beyond big data analytics to include intrusion detection systems. Artificial intelligence and image processing advances are changing and challenging how people interact with digital images and video, and because of their intrinsically contentious character and the reach of contemporary society, they are intended to propagate harmful content and disinformation to millions of people.
A picture may say a thousand words, but what if the photograph has been fabricated? The term "fake news" has recently gained popularity, yet with today's photo manipulation techniques, even the most vigilant eyes can be tricked. One of these areas is the use of several software such as faceapp and fakeapp to create modified media files known as deepfake. From massive data analysis to human biometric systems, deep learning algorithms are used.
Due to their user-friendly characteristics, these applications are growing more popular with the general public and are employed in a range of fields including digital fraud, cybercrime, politics, and even military actions. As a result, it's critical to build detection technologies that can detect and remove this form of forgery, as well as to take a new step forward in video and audio forensics. In this article, we discuss different detection and production methodologies currently used in deepfake research.},
        keywords = {},
        month = {},
        }