Last year, I co-authored a book chapter titled Do Not ‘Fake It Till You Make It’! for Springer Nature Group's Deep Learning for Social Media Data Analytics book series 📕 The publication walks through a comparative study of Deep Learning models to approach the tasks of identifying phony information, verifying the validity of various claims and facts, catching fake content, and so on. It eloquently analyzes the definition of Fake News specifically clickbait, hoax, satire, propaganda, hyperpartisan, and deepfakes in the world of social media, the various forms it can take, what causes its spread, and what are the rudimentary signs of such fake news. It further discusses the limitations of the detection algorithms with insights into the fairness, interpretability, and accountability along with providing pointers for the readers regarding emerging trends in this domain.
My favorite part of the chapter was certainly deep fakes since it was intriguing to dive into the various real-world case studies of their generation, propagation, and detection 🥸 My probing stepped up a level when Packt Publishers sent over a copy of their latest book on this theme titled Exploring Deepfakes: Deploy powerful AI techniques for face replacement and more for review.
Bryan Lyon and Matt Tora have done a wonderful job covering the basics of deepfakes, including their working, history, and potential uses. One of the strengths of the book is its discussion of the ethical implications of Deep Fakes. The authors acknowledge that deepfakes can be used for malicious purposes, such as spreading misinformation or creating explicit audiovisuals for revenge. However, they also argue that deepfakes can be used for positive purposes, such as creating educational content or making films more realistic.
In my opinion, books become more utilitarian when they dispense practical knowledge to their readers. The highlight of this book is the step-by-step instructions on how to create your own deepfakes using Open Source software. The authors have explained the technical concepts clearly and concisely.
The book is well-written, informative, and thought-provoking, even for those with no prior experience with deepfakes or advanced ML. I would recommend checking out this book if you are interested to learn more about deepfakes.