Published Paper Details:

Advanced by sophisticated deep learning algorithms, deepfake technology poses a substantial threat to contemporary society, facilitating the creation of remarkably convincing manipulated media. These manipulations often in-volve superimposing faces onto different bodies or generating fabricated speeches, thereby fueling concerns regarding misinformation, privacy breaches, and potential misuse. This study is dedicated to the creation and assessment of deep learning-based models, including CNN, RNN, ResNext, and LSTM, designed for the detection of deepfake videos. Additionally, we explore the potential challenges and future directions in deepfake detection research, underscoring the imperative need for ongoing advancements in this domain to stay ahead of evolving deepfake generation techniques. Ultimately, this research contributes to the collective endeavor to counteract the misuse of deepfake technology and safeguard the integrity of digital media and public discourse.