Which type of neural network is designed specifically to differentiate between generated images and real images?

The correct choice is a Generative Adversarial Network (GAN), which is specifically designed to differentiate between generated images and real images. GANs consist of two main components: a generator and a discriminator. The generator creates synthetic images, attempting to mimic real images, while the discriminator evaluates images, determining whether they are real or generated. This adversarial process allows the GAN to improve its performance over time, leading ultimately to the generation of highly realistic images.

Convolutional neural networks (CNNs) are primarily utilized for visual data processing and image recognition tasks, but they do not inherently include the adversarial training aspect found in GANs. Long short-term memory networks (LSTMs) and recurrent neural networks (RNNs) are designed for processing sequential data, such as time series or natural language, and are not applicable to tasks solely focused on image generation and differentiation. Therefore, the distinct structure and function of GANs make them the appropriate choice for differentiating between real and generated images.