Part 5 New developments
and challenges
In the final chapter of this book, we step back and discuss the latest advances, challenges, and open questions in text-to-image generation. We begin with an overview of how state-of-the-art models such as OpenAI’s DALL-E series, Google’s Imagen, and Stable Diffusion (and its derivative, Midjourney) have revolutionized the field by translating natural language prompts into detailed, high-fidelity images. Despite these breakthroughs, the chapter emphasizes persistent challenges, such as geometric inconsistency, high computational and environmental costs, misuse through deepfakes, intellectual property disputes, and embedded social biases.
The second half of the chapter focuses on developing a defense mechanism against AI-generated fakes through deep learning. It provides a detailed, hands-on guide to fine-tuning ResNet-50 to classify real versus fake images. The chapter concludes by reinforcing the need for a balanced view, embracing the creative potential of generative AI while responsibly addressing the risks it poses to authenticity, ownership, and trust.