DEEPFAKE IMAGE DETECTION USING DEEP LEARNING

Abstract: The rapid growth of artificial intelligence has made it possible to create images and videos that look increasingly realistic. One of the most noticeable examples of this technology is deepfake media, where a person's face or identity can be digitally changed using artificial intelligence. Although deepfake technology has useful applications in areas such as entertainment, education and digital content creation, it can also be misused to spread false information, impersonate people and manipulate digital evidence Because modern deepfakes can look very convincing, identifying them simply by looking at an image is becoming more difficult. This has led researchers to explore automated detection methods based on machine learning and deep learning. In particular, convolutional neural networks have been widely used because they can learn visual patterns that may not be obvious to human observers.
This literature review examines existing research on deepfake generation and detection, commonly used datasets, deep learning approaches and the challenges faced by current detection systems. It also discusses EfficientNet as a lightweight deep learning architecture and its suitability for developing a practical deepfake image detection system such as DeepGuard.