Improving Facial Recognition: Evaluating CNNs for Detecting Manipulated Images

The ability to distinguish between real and fake imagery is extremely important in the field of computer vision, especially when it comes to facial recognition. In order to detect real faces from fakes, this study explores the creation and assessment of convolutional neural network (CNN) models. Our study compares the performance of well-known CNN architectures, such as VGG, ResNet, and EfficientNetB0, using the TensorFlow framework. After extensive testing and analysis, EfficientNetB0 stands out as the top performer, attaining a previously unheard-of 99% accuracy. This astounding outcome demonstrates the effectiveness of EfficientNetB0's sophisticated architecture, which is tuned for optimal performance in tasks requiring subtle distinction between real and fake facial images.