Fake Image Detector using Machine Learning

The detection of fake images has become increasingly critical due to the proliferation of deepfakes and sophisticated image manipulation techniques used for misinformation, fraud, and public manipulation. This paper offers a thorough examination of various methods for detecting fake images, encompassing both traditional and contemporary strategies. Traditional techniques, including Principal Component Analysis (PCA) and Support Vector Machines (SVMs), focus on the analysis of statistical properties and the identification of inconsistencies in image features; however, these methods frequently encounter challenges with more subtle or complex alterations. In contrast, recent developments in the field harness deep learning methodologies, particularly Convolutional Neural Networks (CNNs), to significantly improve detection accuracy.CNNs excel in learning hierarchical features and detecting intricate patterns indicative of image tampering. Generative Adversarial Networks (GANs) also play a role in this domain by improving model robustness through adversarial training. Despite these advancements, challenges remain, including the evolving nature of manipulation techniques, the need for extensive annotated datasets, and computational demands. This review highlights the strengths and limitations of various methods and suggests future research directions to enhance fake image detection, such as multi-class classification, transfer learning, and real-time application optimizations.