DEEP FAKE DETECTION USING DEEP LEARNING

COMPUTER SCIENCE AND ENGINEERING

The proliferation of deep fake technology has raised significant concerns regarding the authenticity and integrity of visual media and the rise of deep fake technology and its potential implications on society. It highlights the increasing sophistication of deep fake algorithms and their ability to create highly convincing fake content that is difficult to discern from real media. This underscores the urgency of developing robust detection mechanisms to identify and mitigate the spread of deep fakes. Deep fakes, which are synthetic media generated using deep learning techniques, pose a serious threat to various domains, including journalism, politics, and entertainment. To address this challenge, this paper proposes a novel approach for detecting deep fakes in images and videos using Convolutional Neural Networks (CNNs). This paper proposes a novel approach for detecting deepfake images and videos using Convolutional Neural Networks (CNNs). The proposed CNN architecture consists of multiple convolutional layers followed by max-pooling and fully connected layers, allowing it to effectively capture intricate patterns and features indicative of deepfake manipulation. We employ a large dataset comprising both authentic and deepfake images and videos to train the network, enabling it to learn discriminative features and generalize well to unseen data.

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